name: title class: title, middle ## Multi-fidelity active learning for discovering solid-state ionic conductors Alex Hernández-García, Yasmine Benabed, Divya Sharma, Michał Koziarski, Yoshua Bengio, Mickaël Dollé .turquoise[Projet PRF3 (Volet 5 - Conducteurs) · 1er février 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> ] --- ## Active learning for scientific discovery .context[The traditional pipeline (no ML)] .right-column-66[<br>.center[]] .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 ## Active learning for scientific discovery .context[Machine learning in the loop] .right-column-66[<br>.center[]] .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 ## Active learning for scientific discovery .context[Machine learning in the loop] .right-column-66[<br>.center[]] .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[A machine learning model replacing real-world experiments can _only_ provide _linear_ gains.] ] --- count: false ## Active learning for scientific discovery .context[Can we do better than _linear_?<br>An agent in the loop] .right-column-66[<br>.center[]] .left-column-33[ A .highlight1[machine learning **agent**] in the loop can: * .highlight1[learn structure] from the available data, * .highlight1[generalise] to unexplored regions of the search space and * .highlight1[build better queries] ] --- count: false ## Active learning for scientific discovery .context[Can we do better than _linear_?<br>An agent in the loop] .right-column-66[<br>.center[]] .left-column-33[ A .highlight1[machine learning **agent**] in the loop can: * .highlight1[learn structure] from the available data, * .highlight1[generalise] to unexplored regions of the search space and * .highlight1[build better queries] .conclusion[A successful AL pipeline with an ML agent in the loop can provide _exponential_ gains.] ] --- count: false ## Active learning for scientific discovery .context[GFlowNet as the agent.] .right-column-66[<br>.center[]] .left-column-33[ A .highlight1[machine learning **agent**] in the loop can: * .highlight1[learn structure] from the available data, * .highlight1[generalise] to unexplored regions of the search space and * .highlight1[build better queries] .conclusion[A successful AL pipeline with an ML agent in the loop can provide _exponential_ gains.] ] --- name: title class: title, middle ## Multi-fidelity active learning for discovering solid-state ionic conductors Alex Hernández-García, Yasmine Benabed, Divya Sharma, Michał Koziarski, Yoshua Bengio, Mickaël Dollé .turquoise[Projet PRF3 (Volet 5 - Conducteurs) · 1er février 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> ] <