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- W2897388712 endingPage "2586" @default.
- W2897388712 startingPage "2574" @default.
- W2897388712 abstract "The predictive simulation of molecular liquids requires potential energy surface (PES) models that are not only accurate but also computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macroscopic properties. We present a new approach to the systematic approximation of the first-principles PES of molecular liquids using the GAP (Gaussian Approximation Potential) framework. The approach allows us to create potentials at several different levels of accuracy in reproducing the true PES and thus to determine the level of quantum chemistry that is necessary to accurately predict macroscopic properties. We test the approach by building a series of many-body potentials for liquid methane (CH4), which is difficult to model from first principles because its behavior is dominated by weak dispersion interactions with a significant many-body component. The increasing accuracy of the potentials in predicting the bulk density correlates with their fidelity to the true PES, whereas the trend with the empirical potentials tested is surprisingly the opposite. We conclude that an accurate, consistent prediction of its bulk density across wide ranges of temperature and pressure requires not only many-body dispersion but also quantum nuclear effects to be modeled accurately." @default.
- W2897388712 created "2018-10-26" @default.
- W2897388712 creator A5018028862 @default.
- W2897388712 creator A5018776175 @default.
- W2897388712 creator A5025442671 @default.
- W2897388712 creator A5037629153 @default.
- W2897388712 creator A5042476964 @default.
- W2897388712 creator A5077764859 @default.
- W2897388712 date "2019-02-22" @default.
- W2897388712 modified "2023-10-17" @default.
- W2897388712 title "Equation of State of Fluid Methane from First Principles with Machine Learning Potentials" @default.
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