Abdulrahman Aldossary, Jorge Arturo Campos-Gonzalez-Angulo, Sergio Pablo-García, Shi Xuan Leong, Ella Miray Rajaonson, Luca Thiede, Gary Tom, Andrew Wang, Davide Avagliano, Alán Aspuru-Guzik
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引用次数: 0
Abstract
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
计算化学是理解分子和预测化学性质不可或缺的工具。然而,由于难以解决薛定谔方程以及计算成本随分子系统的大小而增加,传统计算方法面临着巨大挑战。为此,人们对利用人工智能(AI)和机器学习(ML)技术进行硅学实验产生了浓厚的兴趣。将人工智能和 ML 集成到计算化学中可以提高化学空间探索的可扩展性和速度。然而,挑战依然存在,特别是在 ML 模型的可重复性和可移植性方面。本综述重点介绍了 ML 在学习、补充或取代传统计算化学进行能量和性质预测方面的演变。从完全基于数值数据训练的模型开始,向结合或学习量子力学物理定律的理想模型迈进。本文还回顾了现有的计算方法和 ML 模型及其相互结合点,概述了未来研究的路线图,并指出了有待改进和创新的领域。最终,我们的目标是开发出能够预测薛定谔方程的准确和可转移解的人工智能架构,从而彻底改变化学和材料科学领域的硅学实验。
期刊介绍:
Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.