Casetti Nicholas, Alfonso-Ramos Javier E, Coley Connor W, Stuyver Thijs
Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139, United States.
Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75005, Paris, France.
Chemistry. 2023 Oct 26;29(60):e202301957. doi: 10.1002/chem.202301957. Epub 2023 Sep 14.
Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.
由机器学习加速的分子量子力学建模，为高通量筛选复杂性质（如化学反应的活化能以及材料和分子的吸收/发射光谱）打开了大门；即通过计算机模拟。在此，我们概述了此类混合计算量子化学/机器学习筛选工作流程中涉及的主要原理、概念和设计考量，并特别强调了它们成功应用的一些近期实例。最后，我们简要展望了将使该领域受益的进一步进展。
