计算机科学
反应性(心理学)
透视图(图形)
领域(数学)
机制(生物学)
组分(热力学)
量子
机器学习
人工智能
数学
认识论
物理
量子力学
医学
热力学
哲学
病理
纯数学
替代医学
作者
Kjell Jorner,Anna Tomberg,Christoph Bauer,Christian Sköld,Per‐Ola Norrby
标识
DOI:10.1038/s41570-021-00260-x
摘要
As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until ‘big data’ applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning. Following a progression from quantum mechanics to modern data-driven methods, this Review presents the methodological spectrum of modelling organic reactions.
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