לוח שנה משולב

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search and generation, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding (quantum) dynamics of molecules and materials? This is an especially relevant question given that quantum mechanical predictions are now within experimental uncertainties for complex molecular systems (https://www.nature.com/articles/s41586-023-06587-3).

Aiming towards a unified machine learning (ML) model of molecular interactions in chemical space, I will discuss the potential and challenges for using ML techniques in chemistry and physics. ML methods can not only accurately estimate molecular properties of large datasets, but they can also lead to new insights into chemical similarity, aromaticity, reactivity, and molecular dynamics. For example, the combination of reliable molecular data with ML methods has enabled a fully quantitative simulation of protein dynamics in water (https://www.science.org/doi/full/10.1126/sciadv.adn4397). While the potential of machine learning for revealing insights into molecules and materials is high, I will conclude my talk by discussing the many remaining avenues for development.

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