Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

• DOI: 10.1021/acs.jctc.8b01242
• Publication Date: 2019-02-22T20:59:13Z
• Authors: Max Veit; Sandeep Kumar Jain; Satyanarayana Bon...; Indranil Rudra; Detlef Hohl; Gábor Csányi
• Keywords: Component (thermodynamics)

The predictive simulation of molecular liquids requires models that are not only accurate, but computationally efficient enough to handle the large systems and long time scales required for reliable prediction macroscopic properties. We present a new approach systematic approximation first-principles potential energy surface (PES) using GAP (Gaussian Approximation Potential) framework. allows us create potentials at several different levels accuracy in reproducing true PES, which test level quantum chemistry is necessary accurately predict its by building liquid methane (CH$_4$), difficult model from first principles because behavior dominated weak dispersion interactions with significant many-body component. find an consistent bulk density across wide range temperature pressure dispersion, also nuclear effects be modeled accurately.