Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement
Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement
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DOI：
10.1021/acs.jpclett.2c03163
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发表时间：
2022-12-05
影响因子：
5.7
通讯作者：
Hoepfner, Michael P.
中科院分区：
文献类型：
--
作者：
Shanks, Brennon L.;Potoff, Jeffrey J.;Hoepfner, Michael P.
从实验中子和 X 射线散射测量中导出可转移对势一直是凝聚态物理学中的一个长期挑战。最先进的散射分析技术通过细化对势来从倒易空间总散射数据估计真实空间微观结构，以获得模拟结果和实验结果之间的一致性。之前通过分子模拟应用这些势的尝试揭示了热力学流体性质的不准确预测。在这封信中，一种应用于稀有气体（Ne、Ar、Kr 和 Xe）的中子散射模式的机器学习辅助结构反演方法被证明可以恢复可转移对势，从而准确地再现从三重点到临界点的微观结构和气液平衡。因此，得出的结论是，单个中子散射测量足以预测各种状态下的宏观热力学性质，并为稠密单原子系统中的局部原子力提供新的见解。
Deriving transferable pair potentials from experimental neutron and X-ray scattering measurements has been a longstanding challenge in condensed matter physics. State-of-the-art scattering analysis techniques estimate real-space microstructure from reciprocal-space total scattering data by refining pair potentials to obtain agreement between simulated and experimental results. Prior attempts to apply these potentials with molecular simulations have revealed inaccurate predictions of thermodynamic fluid properties. In this Letter, a machine learning assisted structure-inversion method applied to neutron scattering patterns of the noble gases (Ne, Ar, Kr, and Xe) is shown to recover transferable pair potentials that accurately reproduce both microstructure and vapor-liquid equilibria from the triple to critical point. Therefore, it is concluded that a single neutron scattering measurement is sufficient to predict macroscopic thermodynamic properties over a wide range of states and provide novel insight into local atomic forces in dense monatomic systems.