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SUMMARY:TYC Highlight Seminar: Atomic-scale machine learning: what do models compute?
DESCRIPTION:Venue: B10\, Molecular Sciences Research Hub\, Imperial College London\, White City Campus \n\n\n\nDirections: https://www.imperial.ac.uk/chemistry/about/molecular-sciences-research-hub/ \n\n\n\n\n\n\n\n\n\n\nTYC Highlight Seminar: Atomic-scale machine learning: what do models compute? Share on X\n\n\n\n\n12:00 – 12:30 – Nan Wu\, PhD student\, Sophia Yaliraki Group\n\n\n\nAtomistic graph learning in allosteric processes \n\n\n\n12:30 – 13:30 – Michele Ceriotti\, EPFL\n\n\n\nAtomic-scale machine learning: what do models compute?Over the past decade\, machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials – in the form of data-driven potentials\, and more generally of surrogate models for all quantities that can be obtained by an electronic-structure calculation. \n\n\n\nApplying machine-learning techniques to simulations has some interesting conceptual implications: if a ML model is to be able to predict the outcome of a physics-based calculation\, it should have sufficient flexibility\, and the appropriate mathematical structure\, to reproduce the desired physical interactions and processes. \n\n\n\nIn this talk I am going to summarize an ongoing effort to better understand the structure of a broad class of ML frameworks that are routinely used in atomistic simulations\, revealing their strengths and limitations. I will discuss how to extract physical insights from a critical analysis of the model performance\, and how to improve the performance of models by incorporating physical-chemical priors. \n\n\n\nI will punctuate this discussion with examples of recent applications of atomistic ML to different classes of materials\, such as high-entropy alloys and ferroelectrics. \n\n\n\n13:00 – 14:00 – Coffee and networking
URL:https://thomasyoungcentre.org/event/tyc-highlight-seminar-atomic-scale-machine-learning-what-do-models-compute-michele-ceriotti-epfl/
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