Palos Etienne, Bull-Vulpe Ethan F, Zhu Xuanyu, Agnew Henry, Gupta Shreya, Saha Suman, Paesani Francesco
Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States.
Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States.
J Chem Theory Comput. 2024 Nov 12;20(21):9269-9289. doi: 10.1021/acs.jctc.4c01005. Epub 2024 Oct 14.
Developing a molecular-level understanding of the properties of water is central to numerous scientific and technological applications. However, accurately modeling water through computer simulations has been a significant challenge due to the complex nature of the hydrogen-bonding network that water molecules form under different thermodynamic conditions. This complexity has led to over five decades of research and many modeling attempts. The introduction of the MB-pol data-driven many-body potential energy function marked a significant advancement toward a universal molecular model capable of predicting the structural, thermodynamic, dynamical, and spectroscopic properties of water across all phases. By integrating physics-based and data-driven (i.e., machine-learned) components, which correctly capture the delicate balance among different many-body interactions, MB-pol achieves chemical and spectroscopic accuracy, enabling realistic molecular simulations of water, from gas-phase clusters to liquid water and ice. In this review, we present a comprehensive overview of the data-driven many-body formalism adopted by MB-pol, highlight the main results and predictions made from computer simulations with MB-pol to date, and discuss the prospects for future extensions to data-driven many-body potentials of generic and reactive molecular systems.
从分子层面理解水的性质对于众多科学和技术应用至关重要。然而，由于水分子在不同热力学条件下形成的氢键网络的复杂性，通过计算机模拟精确建模水一直是一项重大挑战。这种复杂性导致了五十多年的研究以及许多建模尝试。MB-pol数据驱动的多体势能函数的引入标志着朝着能够预测水在所有相中的结构、热力学、动力学和光谱性质的通用分子模型迈出了重要一步。通过整合基于物理和数据驱动（即机器学习）的组件，MB-pol正确捕捉了不同多体相互作用之间的微妙平衡，实现了化学和光谱精度，从而能够对水进行从气相团簇到液态水和冰的逼真分子模拟。在这篇综述中，我们全面概述了MB-pol采用的数据驱动多体形式主义，突出了迄今为止用MB-pol进行计算机模拟得出的主要结果和预测，并讨论了未来将数据驱动多体势扩展到通用和反应性分子系统的前景。
