期刊
CHEMICAL REVIEWS
卷 121, 期 16, 页码 10001-10036出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.chemrev.0c01303
关键词
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资金
- Swiss National Science Foundation [407540_167186 NFP 75]
- European Research Council
- European Union's Horizon 2020 research and innovation programme [952165, 957189]
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [772834]
- NCCR MARVEL - Swiss National Science Foundation
Chemical compound space (CCS) is vast and exploring it using modern machine learning techniques based on quantum mechanics principles can improve computational efficiency while maintaining predictive power. These methods have potential applications in discovering novel molecules or materials with desirable properties.
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.
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