Artificial Intelligence at the Interface of Chemistry and Mathematics

能够通过计算机模拟的唯一手段，预先选择值得进行实验测试的药物，预测特定化合物对DNA的损伤机制，或设计具有特定性能的材料，无论是在技术进步，健康和经济方面，还是在节能方面，都对整个社会具有毫无疑问的意义。尽管化学和生物分子科学取得了所有重要进展，无论是通过量子计算还是机器学习算法，仍然存在一些基本的未解决的问题，阻碍了这些计算的结果和结论的完全可靠性。最先进的机器学习方法和量子化学计算提供了令人印象深刻的结果，为科学，技术和社会开辟了新的视角。然而，它们依赖于大量的计算资源来处理巨大的手动注释数据集。无论是用生物学、化学、物理学还是工程学的语言来表述，所有这些未解决的问题的核心挑战都是数学。可持续和高效的计算算法的发展需要超越边界，并结合联合收割机从不同的科学领域的知识，Gerolin博士的建议CRC研究计划开发新的数学和计算工具，结合数学，机器学习，计算和理论化学的概念，以打破这种理论和计算的限制。该计划的成果可应用于过渡金属（催化的主力），分散相互作用（例如DNA中的氢键相互作用），化学和药物发现。这些严谨的研究可以帮助研究同一问题的其他研究人员消除一些猜测和怀疑，从而形成一种更可持续的科学。跨学科的方法为增进科学界之间的相互理解带来了巨大的希望。

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.