name: mlforscience-udem-feb24 class: title, middle ## Machine learning for science ### Tackling climate and health challenges
## Apprentissage automatique pour les sciences ### S'attaquer à la crise climatique et aux défis en santé Alex Hernández-García (he/il/él) .center[
    
] .smaller[.footer[ Slides: [alexhernandezgarcia.github.io/slides/{{ name }}](https://alexhernandezgarcia.github.io/slides/{{ name }}) ]] --- count: false name: title class: title, middle ## About me .center[![:scale 30%](/assets/images/slides/misc/gaspesie.jpg)] --- ## About me ### My academic journey -- * .highlight1[Bachelor's and Master's]: University Carlos III of Madrid (2009-2105) -- * .highlight1[PhD]: Institute of Cognitive Science, Universität Osnabrück (2016–2020) * Based in Berlin * Marie Skłodowska Curie ITN fellow * Thesis topics: deep learning, visual perception and computational neuroscience * Internship at Spinoza Centre for Neuroimaging, Amsterdam, 2018 * Internship at University of Cambridge, 2019 -- * .highlight1[Postdoc]: Mila and the Université de Montréal (2020–present) * Prof. Yoshua Bengio's group and Prof. David Rolnick's group as IVADO fellow. * Teaching assistant in _Fundamentals of machine learning_, 2021. * Main instructor of _Projets avancés en apprentissage automatique_ (EN x1 and FR x2), 2022-2024. * Mila Lab Representative (2021-2022), EDI Committee (2021-present), Sustainability Committee (2023-present) --- ## About me ### Alongside my academic journey... --- count: false ## About me ### Alongside my academic journey... .center[![:scale 60%](/assets/images/slides/misc/ouc3m.jpg)] --- count: false ## About me ### Alongside my academic journey... .center[![:scale 80%](/assets/images/slides/misc/osi_all.png)] .center[![:scale 30%](/assets/images/slides/misc/osi_alex.png)] .footnote[[Orchestre Symphonique de l'Isle](https://osimontreal.com/)] --- count: false ## About me ### Alongside my academic journey... .center[![:scale 60%](/assets/images/slides/misc/bjr1.jpg)] .footnote[[BrokenJugRamblers.bandcamp.com](https://brokenjugramblers.bandcamp.com/)] --- count: false ## About me ### Alongside my academic journey... .center[![:scale 60%](/assets/images/slides/misc/lume.jpg)] .footnote[[LumeDeBiqueira.es](https://www.lumedebiqueira.es/en/)] --- name: mlforscience-udem-feb24 class: title, middle ## Machine learning for science ### Tackling climate and health challenges
## Apprentissage automatique pour les sciences ### S'attaquer à la crise climatique et aux défis en santé --- count: false name: mlforscience-udem-feb24 class: title, middle ## Machine learning for science ### Tackling climate .highlight2[and] health challenges
## Apprentissage automatique pour les sciences ### S'attaquer à la crise climatique .highlight2[et] aux défis en santé --- count: false name: title class: title, middle ### Why climate and health? .center[![:scale 30%](/assets/images/slides/climatechange/demo.jpg)] --- ## Why climate and health? .context[Climate change is a major challenge for humanity.] .center[
.smaller[Modelled and observed global average temperatures in the last 2 millenia (source graphic:
The Guardian
.)]
] .conclusion["The evidence is clear: the time for action is now." .smaller[IPCC Report, 2022]] --- ## Why climate and health? .context[Climate change is a major challenge for humanity.] .center[
.smaller[Climate-sensitive health risks (Adapted from:
World Health Organization
.)]
] .smaller[ * Environmental factors take the lives of around 13 million people _per year_. * Climate change affects people’s mental and physical health, access to clean air, safe water, food and health care. ] .full-width[ .conclusion["Climate change is the single biggest health threat facing humanity." .smaller[[WHO and WMO](https://climahealth.info/), 2024]] ] --- ## Why climate _and_ health? .center[![:scale 60%](/assets/images/slides/climatechange/climate_health.png)] --- count: false ## Why climate _and_ health? .center[![:scale 60%](/assets/images/slides/climatechange/climate_health_ai.png)] -- .conclusion[Tackling climate change has a direct positive impact on global health. There is a strong synergy between machine learning research for climate and health.] --- ### Overview of my recent work .columns-3-left[.center[.milapaleblue[.smaller[Materials discovery for sustainability]]]] .columns-3-center[.center[.milapalered[.smaller[Drug discovery / Health]]]] .columns-3-right[.center[.milapalegreen[.smaller[Climate]]]] .smaller70[ - .milapalegreen[ClimateGAN: Raising climate change awareness by generating images of floods]. ICLR (2022) - .milapalered[Benchmark of ... for gut microbiome-based diagnosis of inflammatory bowel disease]. Frontiers in Genetics (2022) - .milapalered[Biological sequence design with GFlowNets]. ICML (2022) - .milapalered[Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning]. Journal of Physical Chemistry B (2022) - .milapaleblue[PhAST: Physics-Aware, Scalable, and Task-specific GNNs for accelerated catalyst design]. Under review - .milapaleblue[GFlowNets for AI-driven] .milapalered[scientific discovery]. Digital Discovery (2023) - .milapalegreen[Hard-constrained deep learning for climate downscaling]. JMLR (2023) - .milapalegreen[Fourier neural operators for arbitrary resolution climate data downscaling]. Under review - .milapalegreen[Multi-variable hard physical constraints for climate model downscaling]. AAAI FSS (2023) - .milapalegreen[Counting carbon: a survey of factors influencing the emissions of machine learning]. arXiv preprint (2023) - .milapalered[Multi-objective GFlowNets]. ICML (2023) - .milapaleblue[A theory of continuous] .milapalered[generative flow networks]. ICML (2023) - .milapaleblue[FAENet: Frame averaging equivariant GNN for materials modeling]. ICML (2023) - .milapaleblue[On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions]. AI4Mat Workshop NeurIPS (2023) - .milapalered[Towards equilibrium molecular conformation generation with GFlowNets]. Digital Discovery (2023) - .milapaleblue[Crystal-GFN: sampling crystals with desirable properties and constraints]. AI4Mat Workshop NeurIPS (2023) - .milapalered[Multi-fidelity active learning] .milapaleblue[with GFlowNets]. RealML Workshop NeurIPS (2023) ] --- count: false ### Overview of my recent work .center[![:scale 70%](/assets/images/slides/misc/wordcloud_papers.png)] --- count: false ### Overview of my recent work .columns-3-left[.center[.milapaleblue[.smaller[Materials discovery for sustainability]]]] .columns-3-center[.center[.milapalered[.smaller[Drug discovery / Health]]]] .columns-3-right[.center[.milapalegreen[.smaller[Climate]]]] .smaller70[ - .milapalegreen[ClimateGAN: Raising climate change awareness by generating images of floods]. ICLR (2022) - .milapalered[Benchmark of ... for gut microbiome-based diagnosis of inflammatory bowel disease]. Frontiers in Genetics (2022) - .milapalered[Biological sequence design with GFlowNets]. ICML (2022) - .milapalered[Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning]. Journal of Physical Chemistry B (2022) - .milapaleblue[PhAST: Physics-Aware, Scalable, and Task-specific GNNs for accelerated catalyst design]. Under review - .milapaleblue[GFlowNets for AI-driven] .milapalered[scientific discovery]. Digital Discovery (2023) - .milapalegreen[Hard-constrained deep learning for climate downscaling]. JMLR (2023) - .milapalegreen[Fourier neural operators for arbitrary resolution climate data downscaling]. Under review - .milapalegreen[Multi-variable hard physical constraints for climate model downscaling]. AAAI FSS (2023) - .milapalegreen[Counting carbon: a survey of factors influencing the emissions of machine learning]. arXiv preprint (2023) - .milapalered[Multi-objective GFlowNets]. ICML (2023) - .milapaleblue[A theory of continuous] .milapalered[generative flow networks]. ICML (2023) - .milapaleblue[FAENet: Frame averaging equivariant GNN for materials modeling]. ICML (2023) - .milapaleblue[On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions]. AI4Mat Workshop NeurIPS (2023) - .milapalered[Towards equilibrium molecular conformation generation with GFlowNets]. Digital Discovery (2023) - .milapaleblue[Crystal-GFN: sampling crystals with desirable properties and constraints]. AI4Mat Workshop NeurIPS (2023) - .milapalered[Multi-fidelity active learning] .milapaleblue[with GFlowNets]. RealML Workshop NeurIPS (2023) ] --- count: false ### Overview of my recent work .columns-3-left[.center[.milapaleblue[.smaller[Materials discovery for sustainability]]]] .columns-3-center[.center[.milapalered[.smaller[Drug discovery / Health]]]] .columns-3-right[.center[.milapalegreen[.smaller[Climate]]]] .smaller70[ - .alpha50[.milapalegreen[ClimateGAN: Raising climate change awareness by generating images of floods]. ICLR (2022)] - .alpha50[.milapalered[Benchmark of ... for gut microbiome-based diagnosis of inflammatory bowel disease]. Frontiers in Genetics (2022)] - .alpha50[.milapalered[Biological sequence design with GFlowNets]. ICML (2022)] - .alpha50[.milapalered[Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning]. Journal of Physical Chemistry B (2022)] - .alpha50[.milapaleblue[PhAST: Physics-Aware, Scalable, and Task-specific GNNs for accelerated catalyst design]. Under review] - .alpha50[.milapaleblue[GFlowNets for AI-driven] .milapalered[scientific discovery]. Digital Discovery (2023)] - .alpha50[.milapalegreen[Hard-constrained deep learning for climate downscaling]. JMLR (2023)] - .alpha50[.milapalegreen[Fourier neural operators for arbitrary resolution climate data downscaling]. Under review] - .alpha50[.milapalegreen[Multi-variable hard physical constraints for climate model downscaling]. AAAI FSS (2023)] - .alpha50[.milapalegreen[Counting carbon: a survey of factors influencing the emissions of machine learning]. arXiv preprint (2023)] - .alpha50[.milapalered[Multi-objective GFlowNets]. ICML (2023)] - .alpha50[.milapaleblue[A theory of continuous] .milapalered[generative flow networks]. ICML (2023)] - .alpha50[.milapaleblue[FAENet: Frame averaging equivariant GNN for materials modeling]. ICML (2023)] - .alpha50[.milapaleblue[On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions]. AI4Mat Workshop NeurIPS (2023)] - .alpha50[.milapalered[Towards equilibrium molecular conformation generation with GFlowNets]. Digital Discovery (2023)] - .milapaleblue[Crystal-GFN: sampling crystals with desirable properties and constraints]. AI4Mat Workshop NeurIPS (2023) - .milapalered[Multi-fidelity active learning] .milapaleblue[with GFlowNets]. RealML Workshop NeurIPS (2023) ] --- ## Outline -- - [Machine learning for scientific discoveries to tackle climate and health challenges](#mlforscience) -- - [Gentle introduction to GFlowNets and overview of contributions](#gflownets) -- - [Crystal-GFN: materials discovery](#crystal-gfn) -- - [Multi-fidelity active learning: drug and materials discovery](#mfal) -- - [Challenges and perspectives](#future-work) --- count: false name: mlforscience class: title, middle ### Machine learning for scientific discoveries .center[![:scale 30%](/assets/images/slides/scientific-discovery/laboratory.png)] --- ## Why scientific discovery? ### The potential on sustainability .context["The time for action is now"] -- > "Limiting global warming will require major transitions in the energy sector. This will involve a substantial reduction in fossil fuel use, widespread electrification, .highlight1[improved energy efficiency, and use of alternative fuels (such as hydrogen)]." .cite[IPCC Sixth Assessment Report, 2022] > "Reducing industry emissions will entail coordinated action throughout value chains to promote all mitigation options, including demand management, .highlight1[energy and materials efficiency, circular material flows]." .cite[IPCC Sixth Assessment Report, 2022] --
.conclusion[Mitigation of the climate crisis requires innovation in the materials sector.] ??? Antimicrobial resistance - https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance - https://www.who.int/news-room/feature-stories/detail/donors-making-a-difference--climate-change-and-its-impact-on-health - https://www.who.int/news/item/31-10-2022-who-and-wmo-launch-a-new-knowledge-platform-for-climate-and-health - https://www.who.int/news/item/08-02-2024-who-medically-important-antimicrobial-list-2024 - https://cdn.who.int/media/docs/default-source/gcp/who-mia-list-2024-lv.pdf?sfvrsn=3320dd3d_2 - https://www.who.int/publications/i/item/9789240047655 --- ## Why scientific discovery? ### The potential on health .context[Drug discovery and vaccine development play a crucial role in modern healthcare systems.] .right-column-33[ .center[![:scale 100%](/assets/images/slides/drugs/who_amr.png)] ] --- count: false ## Why scientific discovery? ### The potential on health .context[Drug discovery and vaccine development play a crucial role in modern healthcare systems.] .right-column-33[ .center[![:scale 100%](/assets/images/slides/drugs/who_amr.png)] ] .left-column-66[ .highlight1[Bacterial antimicrobial resistance] contributed to 4.95 million deaths in 2019. .cite[World Health Organisation (WHO), 2023] The World Bank estimates that antimicrobial resistance could result in .highlight1[one _trillion_ USD additional healthcare costs] by 2050. WHO's latest annual review identified 27 antibiotics in clinical development that address WHO bacterial priority pathogens, of which .highlight1[only 6 were classified as innovative]. ] --- count: false ## Why scientific discovery? ### The potential on health .context[Drug discovery and vaccine development play a crucial role in modern healthcare systems.] .right-column-33[ .center[
"No time to wait". Source:
WHO
.
] ] .left-column-66[ .highlight1[Bacterial antimicrobial resistance] contributed to 4.95 million deaths in 2019. .cite[World Health Organisation (WHO), 2023] The World Bank estimates that antimicrobial resistance could result in .highlight1[one _trillion_ USD additional healthcare costs] by 2050. WHO's latest annual review identified 27 antibiotics in clinical development that address WHO bacterial priority pathogens, of which .highlight1[only 6 were classified as innovative]. ] .full-width[ .conclusion["No time to wait". Alongside other necessary actions, drug discovery plays a key role in tackling the antimicrobial resistance global threat.] ] --- ## Traditional discovery cycle .context35[The climate crisis demands accelerating scientific discoveries.] -- .right-column-66[
.center[![:scale 80%](/assets/images/slides/scientific-discovery/loop_1.png)]] .left-column-33[
The .highlight1[traditional pipeline] for scientific discovery: * relies on .highlight1[highly specialised human expertise], * it is .highlight1[time-consuming] and * .highlight1[financially and computationally expensive]. ] --- count: false ## Machine learning in the loop .context35[The traditional scientific discovery loop is too slow for certain applications.] .right-column-66[
.center[![:scale 80%](/assets/images/slides/scientific-discovery/loop_2.png)]] .left-column-33[
A .highlight1[machine learning model] can be: * trained with data from _real-world_ experiments and ] --- count: false ## Machine learning in the loop .context35[The traditional scientific discovery loop is too slow for certain applications.] .right-column-66[
.center[![:scale 80%](/assets/images/slides/scientific-discovery/loop_3.png)]] .left-column-33[
A .highlight1[machine learning model] can be: * trained with data from _real-world_ experiments and * used to quickly and cheaply evaluate queries ] --- count: false ## Machine learning in the loop .context35[The traditional scientific discovery loop is too slow for certain applications.] .right-column-66[
.center[![:scale 80%](/assets/images/slides/scientific-discovery/loop_3.png)]] .left-column-33[
A .highlight1[machine learning model] can be: * trained with data from _real-world_ experiments and * used to quickly and cheaply evaluate queries .conclusion[There are infinitely many conceivable materials, $10^{180}$ potentially stable and $10^{60}$ drug molecules. Are predictive models enough?] ] --- count: false ## _Generative_ machine learning in the loop .right-column-66[
.center[![:scale 80%](/assets/images/slides/scientific-discovery/loop_4.png)]] .left-column-33[
.highlight1[Generative machine learning] can: * .highlight1[learn structure] from the available data, * .highlight1[generalise] to unexplored regions of the search space and * .highlight1[build better queries] ] --- count: false ## _Generative_ machine learning in the loop .right-column-66[
.center[![:scale 80%](/assets/images/slides/scientific-discovery/loop_4.png)]] .left-column-33[
.highlight1[Generative machine learning] can: * .highlight1[learn structure] from the available data, * .highlight1[generalise] to unexplored regions of the search space and * .highlight1[build better queries] .conclusion[Active learning with generative machine learning can in theory more efficiently explore the candidate space.] ] --- count: false name: title class: title, middle ### The challenges of scientific discoveries .center[![:scale 15%](/assets/images/slides/materials/lithium_oxide_crystal.png)] .center[![:scale 30%](/assets/images/slides/dna/dna_helix.png)] --- ## An intuitive trivial problem .highlight1[Problem]: find one arrangement of Tetris pieces on the board that minimise the empty space. .left-column-33[ .center[![:scale 30%](/assets/images/slides/tetris/board_empty.png)] ] .right-column-66[ .center[![:scale 40%](/assets/images/slides/tetris/action_space_minimal.png)] ] -- .full-width[.center[
Score: 12
]] --- count: false ## An intuitive ~~trivial~~ easy problem .highlight1[Problem]: find .highlight2[all] the arrangements of Tetris pieces on the board that minimise the empty space. .left-column-33[ .center[![:scale 30%](/assets/images/slides/tetris/board_empty.png)] ] .right-column-66[ .center[![:scale 40%](/assets/images/slides/tetris/action_space_minimal.png)] ] -- .full-width[.center[
12
12
12
12
12
]] --- count: false ## An intuitive ~~easy~~ hard problem .highlight1[Problem]: find .highlight2[all] the arrangements of Tetris pieces on the board that minimise the empty space. .left-column-33[ .center[![:scale 40%](/assets/images/slides/tetris/10x20/board_empty.png)] ] .right-column-66[ .center[![:scale 80%](/assets/images/slides/tetris/10x20/action_space_all_pieces.png)] ] -- .full-width[.center[
]] --- count: false ## An incredibly ~~intuitive easy~~ hard problem .highlight1[Problem]: find .highlight2[all] the arrangements of Tetris pieces on the board that .highlight2[optimise an unknown function]. .left-column-33[ .center[![:scale 40%](/assets/images/slides/tetris/10x20/board_empty.png)] ] .right-column-66[ .center[![:scale 80%](/assets/images/slides/tetris/10x20/action_space_all_pieces.png)] ] -- .full-width[.center[
]] --- count: false ## An incredibly ~~intuitive easy~~ hard problem .highlight1[Problem]: find .highlight2[all] the arrangements of Tetris pieces on the board that .highlight2[optimise an unknown function]. .left-column-33[ .center[![:scale 40%](/assets/images/slides/tetris/10x20/board_empty.png)] ] .right-column-66[ .center[![:scale 80%](/assets/images/slides/tetris/10x20/action_space_all_pieces.png)] ] .full-width[.conclusion[Materials and drug discovery involve finding candidates with rare properties from combinatorially or infinitely many options.]] --- ## Why Tetris for scientific discovery? .context35[The "Tetris problem" involves .highlight1[sampling from an unknown distribution] in a .highlight1[discrete, high-dimensional, combinatorially large space].] --- count: false ## Why Tetris for scientific discovery? ### Biological sequence design
Proteins, antimicrobial peptides (AMP) and DNA can be represented as sequences of amino acids or nucleobases. There are $22^{100} \approx 10^{134}$ protein sequences with 100 amino acids. .context35[The "Tetris problem" involves sampling from an unknown distribution in a discrete, high-dimensional, combinatorially large space] .center[![:scale 45%](/assets/images/slides/dna/dna_helix_annotated.png)] -- .left-column-66[ .dnag[`G`].dnaa[`A`].dnag[`G`].dnag[`G`].dnag[`G`].dnac[`C`].dnag[`G`].dnaa[`A`].dnac[`C`].dnag[`G`].dnag[`G`].dnat[`T`].dnaa[`A`].dnac[`C`].dnag[`G`].dnag[`G`].dnaa[`A`].dnag[`G`].dnac[`C`].dnat[`T`].dnac[`C`].dnat[`T`].dnag[`G`].dnac[`C`].dnat[`T`].dnac[`C`].dnac[`C`].dnag[`G`].dnat[`T`].dnat[`T`].dnaa[`A`]
.dnat[`T`].dnac[`C`].dnaa[`A`].dnac[`C`].dnac[`C`].dnat[`T`].dnac[`C`].dnac[`C`].dnac[`C`].dnag[`G`].dnaa[`A`].dnag[`G`].dnac[`C`].dnaa[`A`].dnaa[`A`].dnat[`T`].dnaa[`A`].dnag[`G`].dnat[`T`].dnat[`T`].dnag[`G`].dnat[`T`].dnaa[`A`].dnag[`G`].dnag[`G`].dnac[`C`].dnaa[`A`].dnag[`G`].dnac[`C`].dnag[`G`].dnat[`T`].dnac[`C`].dnac[`C`].dnat[`T`].dnaa[`A`].dnac[`C`].dnac[`C`].dnag[`G`].dnat[`T`].dnat[`T`].dnac[`C`].dnag[`G`]
.dnac[`C`].dnat[`T`].dnaa[`A`].dnac[`C`].dnag[`G`].dnac[`C`].dnag[`G`].dnat[`T`].dnac[`C`].dnat[`T`].dnac[`C`].dnat[`T`].dnat[`T`].dnat[`T`].dnac[`C`].dnag[`G`].dnag[`G`].dnag[`G`].dnag[`G`].dnag[`G`].dnat[`T`].dnat[`T`].dnaa[`A`]
.dnat[`T`].dnat[`T`].dnag[`G`].dnac[`C`].dnaa[`A`].dnag[`G`].dnaa[`A`].dnag[`G`].dnag[`G`].dnat[`T`].dnat[`T`].dnaa[`A`].dnaa[`A`].dnac[`C`].dnag[`G`].dnac[`C`].dnag[`G`].dnac[`C`].dnaa[`A`].dnat[`T`].dnag[`G`].dnac[`C`].dnag[`G`].dnaa[`A`].dnac[`C`].dnat[`T`].dnag[`G`].dnag[`G`].dnag[`G`].dnag[`G`].dnat[`T`].dnat[`T`].dnaa[`A`].dnag[`G`].dnat[`T`].dnaa[`A`].dnag[`G`].dnat[`T`].dnac[`C`].dnag[`G`].dnaa[`A`].dnaa[`A`].dnac[`C`].dnaa[`A`].dnat[`T`].dnaa[`A`].dnat[`T`].dnaa[`A`].dnat[`T`].dnat[`T`].dnag[`G`].dnaa[`A`].dnat[`T`].dnaa[`A`].dnaa[`A`].dnaa[`A`].dnac[`C`].dnaa[`A`]
.dnag[`G`].dnac[`C`].dnat[`T`].dnac[`C`].dnag[`G`].dnac[`C`].dnat[`T`].dnat[`T`].dnaa[`A`].dnag[`G`].dnag[`G`].dnag[`G`].dnac[`C`].dnac[`C`].dnat[`T`].dnac[`C`].dnag[`G`].dnaa[`A`].dnac[`C`].dnat[`T`].dnac[`C`].dnac[`C`].dnat[`T`].dnac[`C`].dnat[`T`].dnag[`G`].dnaa[`A`].dnaa[`A`].dnat[`T`].dnag[`G`].dnag[`G`].dnaa[`A`].dnag[`G`].dnat[`T`].dnag[`G`].dnat[`T`].dnat[`T`].dnac[`C`].dnaa[`A`].dnat[`T`].dnac[`C`].dnag[`G`].dnaa[`A`].dnaa[`A`].dnat[`T`].dnag[`G`].dnag[`G`].dnaa[`A`].dnag[`G`].dnat[`T`].dnag[`G`]
] --- ## Why Tetris for scientific discovery? ### Molecular generation .context35[The "Tetris problem" involves sampling from an unknown distribution in a discrete, high-dimensional, combinatorially large space]
Small molecules can also be represented as sequences or by a combination of of higher-level fragments. There may be about $10^{60}$ drug-like molecules. -- .columns-3-left[ .center[ ![:scale 90%](/assets/images/slides/drugs/melatonin.png) `CC(=O)NCCC1=CNc2c1cc(OC)cc2 CC(=O)NCCc1c[nH]c2ccc(OC)cc12` ]] .columns-3-center[ .center[ ![:scale 90%](/assets/images/slides/drugs/thiamine.png) `OCCc1c(C)[n+](cs1)Cc2cnc(C)nc2N` ]] .columns-3-right[ .center[ ![:scale 60%](/assets/images/slides/drugs/nicotine.png) `CN1CCC[C@H]1c2cccnc2` ]] --- ## Machine learning for scientific discovery ### Challenges and limitations of existing methods -- .highlight1[Challenge]: very large and high-dimensional search spaces. -- → Need for .highlight2[efficient search and generalisation] of underlying structure. -- .highlight1[Challenge]: underspecification of objective functions or metrics. -- → Need for .highlight2[diverse] candidates. -- .highlight1[Limitation]: Reinforcement learning excels at optimisation in complex spaces but tends to lack diversity. -- .highlight1[Limitation]: Markov chain Monte Carlo (MCMC) can _sample_ from a distribution (diversity) but struggles at mode mixing in high dimensions. -- → Need to .highlight2[combine all of the above]: sampling from complex, high-dimensional distributions. -- .conclusion[Generative flow networks (GFlowNets) address these challenges.] --- count: false name: gflownets class: title, middle ### A gentle introduction to GFlowNets .center[![:scale 30%](/assets/images/slides/gfn-seq-design/flownet.gif)] --- ## GFlowNets for science ### 3 key ingredients .context[Materials and drug discovery involve .highlight1[sampling from unknown distributions] in .highlight1[discrete or mixed, high-dimensional, combinatorially large spaces.]] --
1. .highlight1[Diversity] as an objective. -- - Given a score or reward function $R(x)$, learn to _sample proportionally to the reward_. -- 2. .highlight1[Compositionality] in the sample generation. -- - A meaningful decomposition of samples $x$ into multiple sub-states $s_0\rightarrow s_1 \rightarrow \dots \rightarrow x$ can yield generalisable patterns. -- 3. .highlight1[Deep learning] to learn from the generated samples. -- - A machine learning model can learn the transition function $F(s\rightarrow s')$ and generalise the patterns. --- ## 1. Diversity as an objective .context[Many existing approaches treat scientific discovery as an _optimisation_ problem.]
Given a reward or objective function $R(x)$, GFlowNet can be seen a generative model trained to sample objects $x \in \cal X$ according to .highlight1[a sampling policy $\pi(x)$ proportional to the reward $R(x)$]: -- .left-column[ $$\pi(x) = \frac{R(x)}{Z} \propto R(x)$$ ] -- .right-column[ $$Z = \sum_{x' \in \cal X} R(x')$$ ] -- .full-width[ .center[ ![:scale 2.5%](/assets/images/slides/tetris/unique_0.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_1.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_2.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_3.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_4.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_5.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_6.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_7.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_8.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_9.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_10.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_11.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_12.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_13.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_14.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_15.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_16.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_17.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_18.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_19.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_20.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_21.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_22.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_23.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_24.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_25.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_26.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_27.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_28.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_29.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_30.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_31.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_32.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_33.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_34.png) ![:scale 2.5%](/assets/images/slides/tetris/unique_35.png) ]] --- count: false ## 1. Diversity as an objective .context[Many existing approaches treat scientific discovery as an _optimisation_ problem.]
Given a reward or objective function $R(x)$, GFlowNet can be seen a generative model trained to sample objects $x \in \cal X$ according to .highlight1[a sampling policy $\pi(x)$ proportional to the reward $R(x)$]: .left-column[ $$\pi(x) = \frac{R(x)}{Z} \propto R(x)$$ ] .right-column[ $$Z = \sum_{x' \in \cal X} R(x')$$ ] .full-width[ → Sampling proportionally to the reward function enables finding .highlight1[multiple modes], hence .highlight1[diversity]. .center[![:scale 22%](/assets/images/slides/gflownet/reward_landscape.png)] ] --- ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] The principle of compositionality is fundamental in semantics, linguistics, mathematical logic and is thought to be a cornerstone of human reasoning. --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. -- .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_0.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_1.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_2.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_3.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_4.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_5.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_6.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_7.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_8.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_9.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_10.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_11.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_12.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_13.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_14.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_15.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_16.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_17.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_18.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_19.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_20.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_21.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_22.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_23.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_24.png)]] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_24.png)]] .right-column[
.conclusion[The decomposition of the sampling process into meaningful steps yields patterns that may be correlated with the reward function and facilitates learning complex distributions.] ] --- count: false ## 2. Compositionality ### Sample generation process .context35[Sampling _directly_ from a complex, high-dimensional distribution is difficult.] For the Tetris problem, a meaningful decomposition of the samples is .highlight1[adding one piece to the board at a time]. .left-column[.center[![:scale 85%](/assets/images/slides/tetris/tree/tree_24.png)]] .right-column[ Objects $x \in \cal X$ are constructed through a sequence of actions from an .highlight1[action space $\cal A$]. ] .right-column[ At each step of the .highlight1[trajectory $\tau=(s_0\rightarrow s_1 \rightarrow \dots \rightarrow s_f)$], we get a partially constructed object $s$ in .highlight1[state space $\cal S$]. ] -- .right-column[ .conclusion[These ideas and terminology is reminiscent of reinforcement learning (RL).] ] --- ## 3. Deep learning policy .context35[GFlowNets learn a sampling policy $\pi\_{\theta}(x)$ proportional to the reward $R(x)$.] -- .left-column[ .center[![:scale 90%](/assets/images/slides/tetris/flows.png)] ] --- count: false ## 3. Deep learning policy .context35[GFlowNets learn a sampling policy $\pi\_{\theta}(x)$ proportional to the reward $R(x)$.] .left-column[ .center[![:scale 90%](/assets/images/slides/tetris/flows_math.png)] ] .right-column[
Deep neural networks are trained to learn the transitions (flows) policy: $F\_{\theta}(s\_t\rightarrow s\_{t+1})$. ] -- .right-column[ Consistent flow theorem (informal): if the sum of the flows into state $s$ is equal to the sum of the flows out, then $\pi(x) \propto R(x)$. ] .references[ Bengio et al. [Flow network based generative models for non-iterative diverse candidate generation](https://arxiv.org/abs/2106.04399), NeurIPS, 2021. (_not_ co-authored) ] -- .right-column[ .conclusion[GFlowNets can be trained with deep learning methods to learn a sampling policy $\pi\_{\theta}$ proportional to a reward $R(x)$.] ] --- ## Contributions to GFlowNets extensions and applications --- count: false ## Contributions to GFlowNets extensions and applications ### Multi-objective GFlowNets Extension of GFlowNets to handle multi-objective optimisation and not only cover the Pareto front but also sample diverse objects at each point in the Pareto front. .center[![:scale 30%](/assets/images/slides/gflownet/mogfn_pareto_front.png)] .references[ Jain et al. [Multi-Objective GFlowNets](https://arxiv.org/abs/2210.12765), ICML, 2023. .highlight2[(co-authored)] ] --- ## Contributions to GFlowNets extensions and applications ### Continuous GFlowNets Generalisation of the theory and implementation of GFlowNets to encompass both discrete and continuous or hybrid state spaces. .center[![:scale 40%](/assets/images/slides/gflownet/cube2d/allvalid.gif)] .references[ Lahlou et al. [A Theory of Continuous Generative Flow Networks](https://arxiv.org/abs/2301.12594), ICML, 2023. .highlight2[(co-authored)] ] --- ## Contributions to GFlowNets extensions and applications ### Molecular conformation generation A continuous GFlowNets algorithm for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule’s energy. .references[ Volokhova, Koziarski et al. [Towards equilibrium molecular conformation generation with GFlowNets](https://arxiv.org/abs/2310.14782), Digital Discovery, 2024. .highlight2[(co-authored)] ] .center[![:scale 100%](/assets/images/slides/conformers/schematic.png)] --- ## Contributions to GFlowNets extensions and applications ### Biological sequence design An active learning algorithm with GFlowNets as a sampler of biological sequence design (DNA, antimicrobial peptides, proteins) with desirable properties. .center[![:scale 45%](/assets/images/slides/dna/dna_helix_annotated.png)] .left-column-66[ .dnag[`G`].dnaa[`A`].dnag[`G`].dnag[`G`].dnag[`G`].dnac[`C`].dnag[`G`].dnaa[`A`].dnac[`C`].dnag[`G`].dnag[`G`].dnat[`T`].dnaa[`A`].dnac[`C`].dnag[`G`].dnag[`G`].dnaa[`A`].dnag[`G`].dnac[`C`].dnat[`T`].dnac[`C`].dnat[`T`].dnag[`G`].dnac[`C`].dnat[`T`].dnac[`C`].dnac[`C`].dnag[`G`].dnat[`T`].dnat[`T`].dnaa[`A`]
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.dnac[`C`].dnat[`T`].dnaa[`A`].dnac[`C`].dnag[`G`].dnac[`C`].dnag[`G`].dnat[`T`].dnac[`C`].dnat[`T`].dnac[`C`].dnat[`T`].dnat[`T`].dnat[`T`].dnac[`C`].dnag[`G`].dnag[`G`].dnag[`G`].dnag[`G`].dnag[`G`].dnat[`T`].dnat[`T`].dnaa[`A`]
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.dnag[`G`].dnac[`C`].dnat[`T`].dnac[`C`].dnag[`G`].dnac[`C`].dnat[`T`].dnat[`T`].dnaa[`A`].dnag[`G`].dnag[`G`].dnag[`G`].dnac[`C`].dnac[`C`].dnat[`T`].dnac[`C`].dnag[`G`].dnaa[`A`].dnac[`C`].dnat[`T`].dnac[`C`].dnac[`C`].dnat[`T`].dnac[`C`].dnat[`T`].dnag[`G`].dnaa[`A`].dnaa[`A`].dnat[`T`].dnag[`G`].dnag[`G`].dnaa[`A`].dnag[`G`].dnat[`T`].dnag[`G`].dnat[`T`].dnat[`T`].dnac[`C`].dnaa[`A`].dnat[`T`].dnac[`C`].dnag[`G`].dnaa[`A`].dnaa[`A`].dnat[`T`].dnag[`G`].dnag[`G`].dnaa[`A`].dnag[`G`].dnat[`T`].dnag[`G`]
] .references[ Jain et al. [Biological Sequence Design with GFlowNets](https://arxiv.org/abs/2203.04115), ICML, 2022. .highlight2[(co-authored)] ] --- ## Contributions to GFlowNets extensions and applications ### Review paper A review of the potential of GFlowNets for AI-driven scientific discoveries. .center[![:scale 60%](/assets/images/slides/drugs/gfn_molecules.png)] .references[ Jain et al. [GFlowNets for AI-Driven Scientific Discovery](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00002h). Digital Discovery, Royal Society of Chemistry, 2023. .highlight2[(co-authored)] ] --- ## GFlowNet Python package I have led the development of an open sourced GFlowNet package, together with Mila collaborators: Nikita Saxena, Alexandra Volokhova, Michał Koziarski, Divya Sharma, Pierre Luc Carrier, Victor Schmidt, Joseph Viviano. .highlight2[Open source GFlowNet implementation]: [github.com/alexhernandezgarcia/gflownet](https://github.com/alexhernandezgarcia/gflownet) -- * A key design principle is the simplicity to create new environments. * Current environments: Tetris, hyper-grid, hyper-cube, hyper-torus, scrabble, crystals, molecules, DNA... * Discrete and continuous environments, multiple loss functions, etc. * Visualisation of results on WandDB --- count: false ## GFlowNet Python package I have led the development of an open sourced GFlowNet package, together with Mila collaborators: Nikita Saxena, Alexandra Volokhova, Michał Koziarski, Divya Sharma, Pierre Luc Carrier Victor Schmidt, Joseph Viviano. .highlight2[Open source GFlowNet implementation]: [github.com/alexhernandezgarcia/gflownet](https://github.com/alexhernandezgarcia/gflownet) Research articles supported by this GFlowNet package: .smaller[ * Lahlou et al. [A Theory of Continuous Generative Flow Networks](https://arxiv.org/abs/2301.12594), ICML, 2023. * Hernandez-Garcia, Saxena et al. [Multi-fidelity active learning with GFlowNets](https://arxiv.org/abs/2306.11715). RealML, NeurIPS 2023. * Mila AI4Science et al. [Crystal-GFN: sampling crystals with desirable properties and constraints](https://arxiv.org/abs/2310.04925). AI4Mat, NeurIPS 2023 (spotlight). * Volokhova, Koziarski et al. [Towards equilibrium molecular conformation generation with GFlowNets](https://arxiv.org/abs/2310.14782). Digital Discovery, NeurIPS 2023. * Several other ongoing projects... ] --- count: false name: crystal-gfn class: title, middle ## Crystal-GFN: GFlowNets for materials discovery Mila AI4Science: Alex Hernandez-Garcia, Alexandre Duval, Alexandra Volokhova, Yoshua Bengio, Divya Sharma, Pierre Luc Carrier, Yasmine Benabed, Michał Koziarski, Victor Schmidt, Pierre-Paul De Breuck .smaller70[Mila AI4Science et al. [Crystal-GFN: sampling crystals with desirable properties and constraints](https://arxiv.org/abs/2310.04925). AI4Mat, NeurIPS 2023 (spotlight) / under review.] .center[![:scale 20%](/assets/images/slides/materials/lithium_oxide_crystal.png)] --- ## What are crystals? Definition: A crystal or crystalline solid is a solid material whose constituents (such as atoms, molecules, or ions) are arranged in a .highlight1[highly ordered microscopic structure], forming .highlight1[a crystal lattice that extends in all directions]. .left-column[ .center[![:scale 70%](/assets/images/slides/crystals/crystals_polycrystalline_amorphous.png)] ] .right-column[ .center[![:scale 30%](/assets/images/slides/materials/lithium_oxide_crystal.png)] ] -- Here, we are concerned mainly with _inorganic crystals_, where the constituents are atoms or ions. -- A crystal structure is characterized by its .highlight1[unit cell], a small imaginary box containing atoms in a specific spatial arrangement with certain symmetry. The unit cell repeats iself periodically in all directions. --- ## Why do we care about crystals? .context35[Materials discovery can help reduce greenhouse gas emissions in multiple sectors.] --
Many solid state materials are crystal structures and they are a core component of: * Electrocatalysts for fuel cells, hydrogen storage, industrial chemical reactions, carbon capture, etc. * Solid electrolytes for batteries. * Thin film materials for photovoltaics. * ... -- However, .highlight1[material modelling is very challenging]: * Limited data: only about 200 K known inorganic materials, but potentially $10^{180}$ possible stable materials (for reference: more than a billion molecules are known) * Sparsity: .highlight2[stable materials] only exist in a low-dimensional subspace of all possible 3D arrangements. -- .conclusion[There is a need for efficient generative models of crystal structures.] --- ## A domain-inspired approach ### Crystal structure parameters .context[Most previous works tackle crystal structure generation in the space of atomic coordinates and struggle to preserve the symmetry properties.] -- Instead of optimising the atom positions by learning from a small data set, we draw .highlight1[inspiration from theoretical crystallography to sample crystals in a lower-dimensional space of crystal structure parameters]. -- .highlight2[Space group]: symmetry operations of a repeating pattern in space that leave the pattern unchanged. -- - There are 17 symmetry groups in 2 dimensions (wallpaper groups). - There are 230 space groups in 3 dimensions. --- count: false ## A domain-inspired approach ### Crystal structure parameters .context[Most previous works tackle crystal structure generation in the space of atomic coordinates and struggle to preserve the symmetry properties.] Instead of optimising the atom positions by learning from a small data set, we draw .highlight1[inspiration from theoretical crystallography to sample crystals in a lower-dimensional space of crystal structure parameters]. .highlight2[Lattice system]: all 230 space groups can be classified into one of the 7 lattices. .center[
Triclinic
Monoclinic
Orthorhombic
Tetragonal
Rhombohedral
Hexagonal
Cubic
] --- count: false ## A domain-inspired approach ### Crystal structure parameters .context[Most previous works tackle crystal structure generation in the space of atomic coordinates and struggle to preserve the symmetry properties.] Instead of optimising the atom positions by learning from a small data set, we draw .highlight1[inspiration from theoretical crystallography to sample crystals in a lower-dimensional space of crystal structure parameters]. .highlight2[Lattice parameters]: The lattice's size and shape is characterised by 6 parameters: .highlight1[$a, b, c, \alpha, \beta, \gamma$]. .center[![:scale 25%](/assets/images/slides/crystals/unit_cell.png)] --- ## Crystal-GFlowNet ### Sequential generation .center[![:scale 40%](/assets/images/slides/tetris/tree/tree_24.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_init.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_sg.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_sg_output.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_comp.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_comp_output.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_lp.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_lp_output.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_all.png)] --- count: false ## Crystal-GFlowNet ### Sequential generation .center[![:scale 100%](/assets/images/slides/crystals/crystalgfn_all.png)] .conclusion[Crystal-GFN binds multiple spaces representing crystallographic and material properties, setting intra- and inter-space hard constraints in the generation process.] --- ## GFlowNet approach ### Advantages .context[We generate materials in the lower-dimensional space of crystal structure parameters.] * Constructing materials by their crystal structure parameters allows us to introduce .highlight1[physicochemical and geometric _hard_ constraints]: -- * Charge neutrality of the composition. * Compatibility of composition and space group. * Hierarchical structure of the space group. * Compatibility of lattice parameters and lattice system. -- * .highlight1[Searching in the lower-dimensional space] of crystal structure parameters may be more efficient than in the space of atom coordinates. -- * Provided we have access to a predictive model of a material property, we can .highlight1[flexibly generate materials with desirable properties]. -- * We can .highlight1[flexibly sample materials with specific characteristics, such as composition or space group]. --- ## Crystal-GFlowNet ### Material properties We can train a Crystal-GFN with any reward function, provided it is computationally tractable. Therefore, we can use it to .highlight1[generate materials with different properties]. -- We have tested the following properties: - .highlight2[Formation energy] per atom [eV/atom], via a pre-trained machine learning model: indicative of the material's stability. -- - .highlight2[Electronic band gap] [eV] (squared distance to a target value, 1.34 eV), via a pre-trained machine learning model: relevant in photovoltaics, for instance. -- - Unit cell .highlight2[density] [g/cm
3
]: convenient as a proof of concept because we can calculate it _exactly_ from the GFN outputs. --- count: false ## Crystal-GFlowNet ### Material properties We can train a Crystal-GFN with any reward function, provided it is computationally tractable. Therefore, we can use it to .highlight1[generate materials with different properties]. We have tested the following properties: - .highlight2[Formation energy] per atom [eV/atom], via a pre-trained machine learning model: indicative of the material's stability. - .highlight2[Electronic band gap] [eV] (squared distance to a target value, 1.34 eV), via a pre-trained machine learning model: relevant in photovoltaics, for instance. - .alpha50[Unit cell .highlight2[density] [g/cm
3
]: convenient as a proof of concept because we can calculate it _exactly_ from the GFN outputs.] --- ## Results ### Formation energy .context35[The formation energy correlates with stability. The lower, the better.] .center[![:scale 70%](/assets/images/slides/crystals/eform_distr_1.png)] --- count: false ## Results ### Formation energy .context35[The formation energy correlates with stability. The lower, the better.] .center[![:scale 70%](/assets/images/slides/crystals/eform_distr_2.png)] --- count: false ## Results ### Formation energy .context35[The formation energy correlates with stability. The lower, the better.] .center[![:scale 70%](/assets/images/slides/crystals/eform_distr_3.png)] --- count: false ## Results ### Formation energy .context35[The formation energy correlates with stability. The lower, the better.] .center[![:scale 70%](/assets/images/slides/crystals/eform_distr_4.png)] --- count: false ## Results ### Formation energy .context[.highlight1[After training, Crystal-GFN samples structures with even lower formation energy [eV/atom] than the validation set.]] .center[![:scale 70%](/assets/images/slides/crystals/eform_distr_4.png)] --- ## Results ### Band gap .context35[We aimed at sampling structures with band gap close to 1.34 eV.] .center[![:scale 70%](/assets/images/slides/crystals/bg_distr_1.png)] --- count: false ## Results ### Band gap .context35[We aimed at sampling structures with band gap close to 1.34 eV.] .center[![:scale 70%](/assets/images/slides/crystals/bg_distr_2.png)] --- count: false ## Results ### Band gap .context35[We aimed at sampling structures with band gap close to 1.34 eV.] .center[![:scale 70%](/assets/images/slides/crystals/bg_distr_3.png)] --- count: false ## Results ### Band gap .context35[We aimed at sampling structures with band gap close to 1.34 eV.] .center[![:scale 70%](/assets/images/slides/crystals/bg_distr_4.png)] --- count: false ## Results ### Band gap .context[.highlight1[After training, Crystal-GFN samples structures with band gap [eV] around the target value.]] .center[![:scale 70%](/assets/images/slides/crystals/bg_distr_4.png)] --- ## Results ### Diversity .context[.highlight2[Diversity] is key in materials discovery.] Analysis of 10,000 sampled crystals and the top-100 with lowest formation energy. -- - All 10,000 samples are unique. -- - All crystal systems, lattice systems and point symmetries found in the 10,000 samples. - 4 out of 8 crystal-lattice systems in the top-100. - 4 out of the 5 point symmetries in the top-100. -- - All 22 elements found in the 10,000 samples. - 15 out of 22 elements in the top-100. -- - 73 out of 113 space groups (65 %) found in the 10,000 samples - 19 out of 113 space groups in the top-100. -- .conclusion[Crystal-GFN samples are highly diverse.] --- ## Crystal-GFN ### Summary and conclusions .references[ * Mila AI4Science et al. [Crystal-GFN: sampling crystals with desirable properties and constraints](https://arxiv.org/abs/2310.04925). AI4Mat, NeurIPS 2023 (spotlight). ] * Discovering new crystal structures with desirable properties can help mitigate the climate crisis. -- * There are infinitely many conceivable crystals. Only a few are stable. Only a few stable crystals have interesting properties. This is a really hard problem. -- * Most methods in the literature struggle to preserve the symmetry properties of the crystals. -- * Crystal-GFN introduces .highlight1[physicochemical and structural constraints], reducing the search space. * Crystal-GFN was trained in 30 hours in a CPU-only machine. -- * Our results show that we can generate .highlight1[diverse, high scoring samples with the desired constraints]. -- * The .highlight1[framework can be flexibly extended] with more constraints, crystal structure descriptors (atomic positions) and other properties. --- count: false name: mfal class: title, middle ## Multi-fidelity active learning Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio .smaller[[Multi-fidelity active learning with GFlowNets](https://arxiv.org/abs/2306.11715). RealML, NeurIPS 2023 / under review.] .center[![:scale 30%](/assets/images/slides/mfal/multiple_oracles.png)] --- ## Why multi-fidelity? .context35[We had described the scientific discovery loop as a cycle with one single oracle.]
.right-column[ .center[![:scale 90%](/assets/images/slides/scientific-discovery/loop_4.png)] ] -- .left-column[ Example: "incredibly hard" Tetris problem: find arrangements of Tetris pieces that optimise an .highlight2[unknown function $f$]. - $f$: Oracle, cost per evaluation 1000 CAD. .center[
] ] --- count: false ## Why multi-fidelity? .context35[However, in practice, multiple oracles (models) of different fidelity and cost are available in scientific applications.]
.right-column[ .center[![:scale 95%](/assets/images/slides/scientific-discovery/loop_4_mf.png)] ] .left-column[ Example: "incredibly hard" Tetris problem: find arrangements of Tetris pieces that optimise an .highlight2[unknown function $f$]. - $f$: Oracle, cost per evaluation 1000 CAD. .center[
] ] --- count: false ## Why multi-fidelity? .context35[However, in practice, multiple oracles (models) of different fidelity and cost are available in scientific applications.]
.right-column[ .center[![:scale 95%](/assets/images/slides/scientific-discovery/loop_4_mf.png)] ] .left-column[ Example: "incredibly hard" Tetris problem: find arrangements of Tetris pieces that optimise an .highlight2[unknown function $f$]. - $f$: Oracle, cost per evaluation 1000 CAD. - $f\_1$: Slightly inaccurate oracle, cost 100 CAD. - $f\_2$: Noisy but informative oracle, cost 1 CAD. .center[
] ] --- count: false ## Why multi-fidelity? .context[In many scientific applications we have access to multiple approximations of the objective function.] .left-column[ For example, in .highlight1[material discovery]: * .highlight1[Synthesis] of a material and characterisation of a property in the lab * Quantum mechanic .highlight1[simulations] to estimate the property * .highlight1[Machine learning] models trained to predict the property ] .right-column[ .center[![:scale 90%](/assets/images/slides/scientific-discovery/loop_4_mf.png)] ] -- .conclusion[However, current machine learning methods cannot efficiently leverage the availability of multiple oracles and multi-fidelity data. Especially with .highlight1[structured, large, high-dimensional search spaces].] --- ## Contribution - An .highlight1[active learning] algorithm to leverage the availability of .highlight1[multiple oracles at different fidelities and costs]. -- - The goal is two-fold: 1. Find high-scoring candidates 2. Candidates must be diverse -- - Experimental evaluation with .highlight1[biological sequences and molecules]: - DNA - Antimicrobial peptides - Small molecules - Classical multi-fidelity toy functions (Branin and Hartmann) -- .conclusion[Likely the first multi-fidelity active learning method for biological sequences and molecules.] --- ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_0.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_1.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_2.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_3.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_4.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_5.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_6.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_7.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_8.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_9.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_10.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_11.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_12.png)] --- count: false ## Our multi-fidelity active learning algorithm .center[![:scale 100%](/assets/images/slides/mfal/mfal_13.png)] --- ## Experiments ### Baselines .context[This is the .highlight1[first multi-fidelity active learning algorithm tested on biological sequence design and molecular design problems]. There did not exist baselines from the literature.] --
* .highlight1[SF-GFN]: GFlowNet with highest fidelity oracle to establish a benchmark for performance without considering the cost-accuracy trade-offs. -- * .highlight1[Random]: Quasi-random approach where the candidates and fidelities are picked randomly and the top $(x, m)$ pairs scored by the acquisition function are queried. -- * .highlight1[Random fid. GFN]: GFlowNet with random fidelities, to investigate the benefit of deciding the fidelity with GFlowNets. -- * .highlight1[MF-PPO]: Replacement of MF-GFN with a reinforcement learning algorithm to _optimise_ the acquisition function. --- ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic electron affinity (EA). Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. -- .center[![:scale 50%](/assets/images/slides/mfal/molecules_ea_1.png)] --- count: false ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic electron affinity (EA). Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. .center[![:scale 50%](/assets/images/slides/mfal/molecules_ea_2.png)] --- count: false ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic electron affinity (EA). Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. .center[![:scale 50%](/assets/images/slides/mfal/molecules_ea_3.png)] --- count: false ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic electron affinity (EA). Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. .center[![:scale 50%](/assets/images/slides/mfal/molecules_ea_4.png)] --- count: false ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic electron affinity (EA). Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. .center[![:scale 50%](/assets/images/slides/mfal/molecules_ea_5.png)] --- count: false ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic electron affinity (EA). Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. .center[![:scale 50%](/assets/images/slides/mfal/molecules_ea_6.png)] --- count: false ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic electron affinity (EA). Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. .center[![:scale 50%](/assets/images/slides/mfal/molecules_ea_7.png)] --- count: false ## Small molecules - Realistic experiments with experimental oracles and costs that reflect the computational demands (1, 3, 7). - GFlowNet adds one SELFIES token (out of 26) at a time with variable length up to 64 ($|\mathcal{X}| > 26^{64}$). - Property: Adiabatic .highlight1[ionisation potential (IP)]. Relevant in organic semiconductors, photoredox catalysis and organometallic synthesis. .center[![:scale 50%](/assets/images/slides/mfal/molecules_ip.png)] --- ## DNA aptamers - GFlowNet adds one nucleobase (`A`, `T`, `C`, `G`) at a time up to length 30. This yields a design space of size $|\mathcal{X}| = 4^{30}$. - The objective function is the free energy estimated by a bioinformatics tool. - The (simulated) lower fidelity oracle is a transformer trained with 1 million sequences. -- .center[![:scale 50%](/assets/images/slides/mfal/dna_6.png)] --- count: false ## Antimicrobial peptides (AMP) - Protein sequences (20 amino acids) with variable length (max. 50). - The oracles are 3 ML models trained with different subsets of data. -- .center[![:scale 60%](/assets/images/slides/mfal/amp.png)] --- ## Multi-fidelity active learning with GFlowNets ### Summary and conclusions .references[ * Hernandez-Garcia, Saxena et al. [Multi-fidelity active learning with GFlowNets](https://arxiv.org/abs/2306.11715). RealML, NeurIPS 2023. ] * Current ML for science methods do not utilise all the information and resources at our disposal. -- * AI-driven scientific discovery demands learning methods that can .highlight1[efficiently discover diverse candidates in combinatorially large, high-dimensional search spaces]. -- * .highlight1[Multi-fidelity active learning with GFlowNets] enables .highlight1[cost-effective exploration] of large, high-dimensional and structured spaces, and discovers multiple, diverse modes of black-box score functions. -- * This is to our knowledge the first algorithm capable of effectively leveraging multi-fidelity oracles to discover diverse biological sequences and molecules. --- count: false name: future-work class: title, middle ## Challenges and perspectives .center[![:scale 30%](/assets/images/slides/misc/challenges_perspective.jpg)] --- ## What's next? ### The short term -- Combine Crystal-GFN and multi-fidelity active learning to tackle .highlight1[specific materials discovery problems with impact in sustainability]: .smaller[ - Solid electrolytes with high ionic conductivity for batteries - Collaboration with Mickaël Dollé and Michel Côté (Département de chimie, UdeM) and National Research Council (NRC) - Challenges: - Very limited data: 600 materials - Very expensive mid-fidelity oracle: 270 supercomputer hours per material ] --- count: false ## What's next? ### The short term Combine Crystal-GFN and multi-fidelity active learning to tackle .highlight1[specific materials discovery problems with impact in sustainability]: .smaller[ - Solid electrolytes with high ionic conductivity for batteries - Collaboration with Mickaël Dollé and Michel Côté (Département de chimie, UdeM) and National Research Council (NRC) - Challenges: - Very limited data: 600 materials - Very expensive mid-fidelity oracle: 270 supercomputer hours per material - Electrocatalysts for hydrogen storage and other applications - We have recently proposed an efficient equivariant GNN model for electrocatalysis (ICML) - Challenges: - Even more complex objects than crystals, bound to specific reactions, etc. - Very expensive oracles - Multiple and underspecificed objectives: efficiency, reaction rate, durability, cost, etc. ] .references[ Duval, Schmidt et al. [FAENet: Frame averaging equivariant GNN for materials modeling](https://arxiv.org/abs/2305.05577). ICML (2023) .highlight2[(co-authored)] ] --- ## What's next? ### The mid term -- The integration of predictive machine learning, generative models, active learning, robotics, causality and quantum computing with domain expertise: .highlight2["self-driving" laboratories] to tackle climate and health challenges. -- - Opportunities: The Université de Montréal offers .highlight1[a unique ecosystem for these research directions]. -- - Challenges: - Need for pushing the research boundaries in multiple areas: uncertainty estimation, causality, human-computer interaction, active learning, specialised generative models... - Automated research must provide **robustness** and safety guarantees. - **Responsible** research must be a guiding principle. -- .conclusion[My future research will further explore domain-inspired generative machine learning methods for climate and health applications, and with interdisciplinary collaborations at the core.] --- ## Acknowledgements .columns-3-left[ Victor Schmidt
Mélisande Teng
Alexandre Duval
Yasmine Benabed
Pierre Luc Carrier
Divya Sharma
Yoshua Bengio
Lena Simine
Michael Kilgour
... ] .columns-3-center[ Alexandra Volokhova
Michał Koziarski
Paula Harder
David Rolnick
Qidong Yang
Santiago Miret
Sasha Luccioni
Alexia Reynaud
Tianyu Zhang
... ] .columns-3-right[ Nikita Saxena
Moksh Jain
Cheng-Hao Liu
Kolya Malkin
Tristan Deleu
Salem Lahlou
Alvaro Carbonero
José González-Abad
Emmanuel Bengio
... ] .conclusion[Science is a lot more fun when shared with bright and interesting people!] --- count: false name: title class: title, middle ## Overall summary and conclusions .center[![:scale 30%](/assets/images/slides/misc/conclusion.png)] --- ## Summary and conclusions - Tackling the climate crisis _is_ tackling health challenges. -- - Machine learning has great potential to accelerate scientific discoveries. There are strong synergies between materials discovery and drug discovery methods. -- - With GFlowNets, we are able to address some important challenges: discover diverse candidates in very large, complex search spaces. -- - Crystal-GFN rethinks crystal structure generation by introducing domain knowledge and hard constraints to discover materials with desirable properties. -- - Multi-fidelity active learning with GFlowNets effectively leverages the availability of multiple oracles for the first time for certain scientific discovery problems. --- name: mlforscience-udem-feb24 class: title, middle ![:scale 40%](/assets/images/slides/climatechange/climate_health_ai.png) Alex Hernández-García (he/il/él) .center[
    
] .footer[[alexhernandezgarcia.github.io](https://alexhernandezgarcia.github.io/) | [alex.hernandez-garcia@mila.quebec](mailto:alex.hernandez-garcia@mila.quebec)]
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