Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario, Canada.
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Nat Mater. 2021 Jun;20(6):750-761. doi: 10.1038/s41563-020-0777-6. Epub 2021 May 27.
The choice of simulation methods in computational materials science is driven by a fundamental trade-off: bridging large time- and length-scales with highly accurate simulations at an affordable computational cost. Venturing the investigation of complex phenomena on large scales requires fast yet accurate computational methods. We review the emerging field of machine-learned potentials, which promises to reach the accuracy of quantum mechanical computations at a substantially reduced computational cost. This Review will summarize the basic principles of the underlying machine learning methods, the data acquisition process and active learning procedures. We highlight multiple recent applications of machine-learned potentials in various fields, ranging from organic chemistry and biomolecules to inorganic crystal structure predictions and surface science. We furthermore discuss the developments required to promote a broader use of ML potentials, and the possibility of using them to help solve open questions in materials science and facilitate fully computational materials design.
在可承受的计算成本下，用高度精确的模拟方法来弥合大时间和大尺度的差距。要在大范围内探索复杂现象，就需要快速而准确的计算方法。我们回顾了机器学习势这一新兴领域，它有望以大大降低的计算成本达到量子力学计算的精度。这篇综述将总结基础机器学习方法的基本原理、数据获取过程和主动学习过程。我们强调了机器学习势在从有机化学和生物分子到无机晶体结构预测和表面科学等各个领域的多个最新应用。此外，我们还讨论了促进更广泛地使用 ML 势所需的发展，以及使用它们来帮助解决材料科学中的开放性问题并促进全计算材料设计的可能性。
