Nature Reviews Chemistry ( IF 38.1 ) Pub Date : 2022-08-25 , DOI: 10.1038/s41570-022-00416-3
Nikita Fedik 1, 2, 3 , Roman Zubatyuk 4 , Maksim Kulichenko 1, 3 , Nicholas Lubbers 5 , Justin S Smith 1, 6 , Benjamin Nebgen 1 , Richard Messerly 1 , Ying Wai Li 5 , Alexander I Boldyrev 3 , Kipton Barros 1, 2 , Olexandr Isayev 4 , Sergei Tretiak 1, 2, 7
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Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.
中文翻译:
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将机器学习扩展到原子间势之外以预测分子特性
机器学习 (ML) 正在成为对复杂化学过程和材料进行建模的首选方法。ML 提供了一个在参考数据集上训练的替代模型,可用于建立分子结构与其化学性质之间的关系。本综述重点介绍了使用 ML 评估化学性质(例如部分原子电荷、偶极矩、自旋和电子密度以及化学键合)以及获得简化的量子力学描述的进展。我们概述了几种现代神经网络架构、它们的预测能力、通用性和可转移性,并说明了它们对各种化学性质的适用性。我们强调学习到的分子表示类似于量子力学类似物,展示模型捕获基础物理的能力。我们还讨论了 ML 模型如何描述非局部量子效应。最后,我们总结了一份可用的 ML 工具箱列表,总结了未解决的挑战并提出了未来发展的展望。观察到的趋势表明,该领域正在朝着由 ML 增强的基于物理的模型发展,伴随着新方法的开发和用户友好的化学 ML 框架的快速增长。