Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery
Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery
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结合分子量子力学建模和机器学习来加速反应筛选和发现
DOI：
10.1002/chem.202301957
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Stuyver, Thijs
中科院分区：
文献类型：
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作者：
Casetti, Nicholas;Alfonso‐Ramos, Javier E.;Coley, Connor W.;Stuyver, Thijs
通过机器学习加速的分子量子力学建模为复杂性质的高通量筛选活动打开了大门，例如化学反应的活化能以及材料和分子的吸收/发射光谱;在计算机上。 在这里，我们概述了这种混合计算量子化学/机器学习筛选工作流程中涉及的主要原理，概念和设计考虑因素，并特别强调了最近成功应用的一些例子。最后，我们简要展望了将有利于该领域的进一步进展。
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.