Artificial Intelligence at the Interface of Chemistry and Mathematics

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Being able, by sole means of computer simulations, to make a preselection of pharmaceutical drugs that deserve to be tested experimentally, to predict the mechanisms of DNA damage by specific compounds, or to design materials with specific properties, is of unquestionable interest for the whole society, in terms of both technological progress, health and economic and energetic saving.Despite all important advances in chemical and biomolecular sciences, there are still basic unsolved problems that hamper a complete reliability of the results and conclusions of such calculations, whether they are obtained via quantum computations or Machine Learning algorithms.The state-of-the-art Machine Learning methods and Quantum Chemistry calculations provide impressive results, opening new perspectives for science, technology, and society. However, they rely on massive computational resources to process huge manually annotated data-sets. The corresponding costs in terms of energy consumption and human efforts are not sustainable.Whether phrased in the language of biology, chemistry, physics, or engineering, the central challenge to all these unsolved problems is mathematical. The development of sustainable and efficient computational algorithms require transcending boundaries and to combine sophisticate knowledge from different scientific areas.Dr. Gerolin's proposed CRC research program develop novel mathematical and computational tools combining concepts from Mathematics, Machine Learning, Computational and Theoretical Chemistry with the goal to break such theoretical and computational limitations. The outcomes of this program can be applied to transition metals (which are the workhorse of catalysis), dispersion interactions (e.g. hydrogen-bonding interaction in DNA), chemical and drug discovery. The rigorous studies developed could help other researchers who work on the same problem to eliminate some guessing and empiricism, leading to a more sustainable science. The interdisciplinary approach holds tremendous promise to increase the mutual understanding between scientific communities.