Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

原子模拟已成为工业应用的宝贵工具，从药物发现的蛋白质-配体相互作用的优化到能源应用新材料的设计。在这里，我们回顾了使用机器学习 (ML) 方法基于系统的量子力学 (QM) 描述进行加速模拟的最新进展。我们展示了机器学习方法的最新进展如何极大地扩展了传统的基于 QM 的模拟的适用范围，从而能够以更高的精度、更低的计算成本以及其他方式无法获得的长度和时间尺度来计算工业相关属性。我们通过展示药物发现（制药）和能源材料这两个截然不同的领域的相关应用，说明了机器学习加速原子模拟对工业研发过程的好处。这篇综述从分子和材料建模科学家的角度撰写，旨在提供机器学习加速原子模拟对制药、化学和材料行业影响的统一图片，并展望可能出现的令人兴奋的机会出现在未来。