name: title class: title, middle ## Crystal-GFN ### Sampling crystals with desirable properties and constraints Alex Hernández-García (he/il/él) .smaller[Divya Sharma, Michał Koziarski, Alexandra Volokhova, Victor Schmidt, Alexandre Duval, Pierre Luc Carrier, Yasmine Benabed] .turquoise[Mila GFlowNet Workshop · Montreal · November 10th 2023] .center[ <a href="https://mila.quebec/"><img src="../assets/images/slides/logos/mila-beige.png" alt="Mila" style="height: 4em"></a>      <a href="https://www.umontreal.ca/"><img src="../assets/images/slides/logos/udem-white.png" alt="UdeM" style="height: 4em"></a> ] .smaller[.footer[ Slides: [alexhernandezgarcia.github.io/slides/crystal-gfn-nov23](https://alexhernandezgarcia.github.io/slides/crystal-gfn-nov23) ]] --- ## What are crystals? [Wikipedia](https://en.wikipedia.org/wiki/Crystal): 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[] ] .right-column[ .center[] ] -- 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? Many solid state materials are crystal structures and they are a core component of .highlight1[solar cells, batteries, electrocatalysts], etc. -- Accelerating .highlight1[material discovery is key in the climate crisis] .cite[IPCC Sixth Assessment Report, 2022]: * Improving material efficiency can reduce 0.93 ($\pm$ 0.23) GtCO₂-eq per year. * Fuel switching can reduce 2.1 ($\pm$ 0.52) GtCO₂-eq per year, only in the industry sector. * Carbon capture and storage can reduce 0.54 ($\pm$ 0.27) GtCO₂-eq per year in the energy sector. .smaller[.footnote[† Global anthropogenic emissions in 2019 were estimated in 59 ($\pm$ 6.6) GtCO₂-eq. The budget from 2020 to limit warming to 1.5°C is estimated in 510 ($\pm$ 180) GtCO₂-eq.]] -- 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. --- ## Related work Example: .highlight2[Crystal Diffusion Variational Autoencoder (CDVAE)]: a diffusion process that moves .highlight1[atomic coordinates] towards a lower energy state and updates atom types to satisfy bonding preferences between neighbors. The key idea is to learn the diffusion process from the data distribution of stable materials. .cite[(Xie et al., 2022)] .center[] .references[Xie et al. [Crystal diffusion variational autoencoder for periodic material generation](https://arxiv.org/abs/2110.06197). ICLR 2022] ??? A: atom types X: atom coordinates L: perdiodic lattice: l1, l2, l3 (3x3) --- ## Our proposal ### Crystal structure parameters .context[CDVAE and other works tackle crystal structure generation in the space of atom coordinates.] 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]. .left-column[ .center[] ] .right-column[ .center[] ] .conclusion[The symmetry of a crystal is determined by one 230 possible space groups. The shape and size of the unit cell is defined by 6 parameters: $a, b, c, \alpha, \beta, \gamma$.] --- ## GFlowNet approach ### Advantages .context[We generate materials in the lower-dimensional space of crystal structure parameters.] * Materials represented by their crystal structure parameters are still .highlight1[predictive of relevant properties]. * Constructing materials by their crystal structure parameters allows us to introduce .highlight1[chemical, physical and geometric constraints]. * .highlight1[Searching in the lower-dimensional space] of crystal structure parameters may be more efficient than in the space of atom coordinates. .center[] --- ## Crystal-GFlowNet ### Main properties * .highlight1[State space]: 1. **Composition**: how many atoms of which elements (discrete) 2. **Space group**: 230 groups, subdivided into crystal system, lattice system, point symmetry and space group (discrete) 3. **Lattice parameters**: 6 parameters, $a, b, c, \alpha, \beta, \gamma$ (continuous) * .highlight1[Action space]: discrete and continuous movements in the subspace of the state space, with constraints. * .highlight1[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. --- ## Results .center[] --- count: false ## Results .center[] --- count: false ## Results .context[10,000 crystals randomly sampled.] .center[] --- count: false ## Results .context[10,000 crystals sampled from a trained GFlowNet.] .center[] --- name: title class: title, middle ## Summary and conclusions .center[] --- ## Summary and conclusions * 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. * GFlowNets enable the introduction of .highlight1[geometrical and physical constraints] to reduce the search space. * Our results show that we can sample diverse, high scoring samples with the desired constraints. .references[ * Mila AI4Science et al. [Crystal-GFN: sampling crystals with desirable properties and constraints](https://arxiv.org/abs/2310.04925). AI4Mat, NeurIPS 2023 (spotlight). * Hernandez-Garcia, Saxena et al. [Multi-fidelity active learning with GFlowNets](https://arxiv.org/abs/2306.11715). RealML, NeurIPS 2023. * Jain et al. [GFlowNets for AI-Driven Scientific Discovery](https://arxiv.org/abs/2302.00615). Digital Discovery, Royal Society of Chemistry, 2023. ] -- .highlight2[Open source code]: [github.com/alexhernandezgarcia/gflownet](https://github.com/alexhernandezgarcia/gflownet) --- ## Acknowledgements .left-column[ * Alexandre Duval * Alexandra Volokhova * Pierre Luc Carrier * Divya Sharma * Victor Schmidt * Yasmine Benabed * Michał Koziarski * Yoshua Bengio * ... ] --- name: title class: title, middle  Alex Hernández-García (he/il/él) .center[ <a href="https://mila.quebec/"><img src="../assets/images/slides/logos/mila-beige.png" alt="Mila" style="height: 3em"></a>      <a href="https://www.umontreal.ca/"><img src="../assets/images/slides/logos/udem-white.png" alt="UdeM" style="height: 3em"></a> ] .footer[[alexhernandezgarcia.github.io](https://alexhernandezgarcia.github.io/) | [alex.hernandez-garcia@mila.quebec](mailto:alex.hernandez-garcia@mila.quebec)]<br> .footer[[@alexhg@scholar.social](https://scholar.social/@alexhg) [](https://scholar.social/@alexhg) | [@alexhdezgcia](https://twitter.com/alexhdezgcia) [](https://twitter.com/alexhdezgcia)] <