Learning molecular potentials with neural networks

人工神经网络 计算机科学 困境 人工智能 机器学习 领域(数学) 神经系统网络模型 计算模型 多样性(控制论) 数据科学 循环神经网络 人工神经网络的类型 数学 几何学 纯数学
作者
Hatice Gökcan,Olexandr Isayev
出处
期刊:Wiley Interdisciplinary Reviews: Computational Molecular Science [Wiley]
卷期号:12 (2) 被引量:36
标识
DOI:10.1002/wcms.1564
摘要

Abstract The potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry. However, the proper choice of the computational approach based on computational cost and reliability of calculated energies is a dilemma, especially for large molecules. This dilemma is proved to be even more problematic for studies that require hundreds and thousands of calculations, such as drug discovery. On the other hand, driven by their pattern recognition capabilities, neural networks started to gain popularity in the computational chemistry community. During the last decade, many neural network potentials have been developed to predict a variety of chemical information of different systems. Neural network potentials are proved to predict chemical properties with accuracy comparable to quantum mechanical approaches but with the cost approaching molecular mechanics calculations. As a result, the development of more reliable, transferable, and extensible neural network potentials became an attractive field of study for researchers. In this review, we outlined an overview of the status of current neural network potentials and strategies to improve their accuracy. We provide recent examples of studies that prove the applicability of these potentials. We also discuss the capabilities and shortcomings of the current models and the challenges and future aspects of their development and applications. It is expected that this review would provide guidance for the development of neural network potentials and the exploitation of their applicability. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Interactions Software > Molecular Modeling
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