Bulk methane models and simulation parameters

GAP machine learning potentials created for simulating condensed-phase bulk methane at the quantum mechanical level (M. Veit, S. K. Jain, S. Bonakala, I. Rudra, D. Hohl, G. Csányi, "Equation of State of Fluid Methane from First Principles with Machine Learning Potentials", J Chem Theory Comput (2019): https://pubs.acs.org/doi/10.1021/acs.jctc.8b01242). Simulation parameters for the NPT and PIMD MD simulations are also included, as are the quantum mechanical source data and fitting parameters.

本数据集包含专为量子力学层面模拟凝聚相本体甲烷而构建的GAP机器学习势（GAP machine learning potentials），相关研究成果见于M. Veit、S. K. Jain、S. Bonakala、I. Rudra、D. Hohl、G. Csányi于2019年发表于《化学理论与计算杂志（Journal of Chemical Theory and Computation）》的论文《基于机器学习势的第一性原理流体甲烷状态方程》，原文链接为https://pubs.acs.org/doi/10.1021/acs.jctc.8b01242。此外，本数据集还涵盖NPT系综与路径积分分子动力学（Path Integral Molecular Dynamics, PIMD）模拟的相关参数，同时包含量子力学源数据与拟合参数。