Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure. The NNs are trained with data from molecular dynamics simulations of monoatomic gases and liquids, sampled in the NVT ensemble at various densities. We find that the NNs provide much more accurate results compared to the analytic low-density limit estimate of the second virial coefficient and the Carnahan–Starling equation of state for hard sphere liquids. Furthermore, we design and train NNs for computing (effective) pair potentials from radial pair distribution functions, g(r), a task that is often performed for inverse design and coarse-graining. Providing the NNs with additional information on the forces greatly improves the accuracy of the predictions since more correlations are taken into account; the predicted potentials become smoother, are significantly closer to the target potentials, and are more transferable as a result.
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28 March 2021
Research Article|
March 29 2021
BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks
Fabian Berressem
;
Fabian Berressem
Institute of Physics, Johannes Gutenberg University Mainz
, Staudingerweg 7, 55128 Mainz, Germany
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Arash Nikoubashman
Arash Nikoubashman
a)
Institute of Physics, Johannes Gutenberg University Mainz
, Staudingerweg 7, 55128 Mainz, Germany
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
a)Author to whom correspondence should be addressed: [email protected]
J. Chem. Phys. 154, 124123 (2021)
Article history
Received:
January 26 2021
Accepted:
March 08 2021
Citation
Fabian Berressem, Arash Nikoubashman; BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks. J. Chem. Phys. 28 March 2021; 154 (12): 124123. https://doi.org/10.1063/5.0045441
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