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Updated: Jun 27, 2025
11:03
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
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Improving the reliability of machine learned potentials for modeling inhomogeneous liquids
Kamron Fazel1, Nima Karimitari2, Tanooj Shah1
1Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA.
Journal of Computational Chemistry
|April 25, 2024
Summary
Neural network potentials (NNPs) accurately predict fluid behavior at interfaces when trained on relevant data. This approach enhances simulations of chemical and biological processes without empirical potentials.
Area of Science:
- Computational chemistry
- Materials science
- Physical chemistry
Background:
- Atomic-scale fluid behavior at interfaces is crucial for chemical, electrochemical, and biological processes.
- Classical molecular dynamics (MD) simulations offer direct insights but are limited by interatomic potential accuracy.
Purpose of the Study:
- To develop and validate neural network potentials (NNPs) for accurately predicting inhomogeneous fluid responses.
- To establish a framework for first-principles simulation of fluids, bypassing empirical potentials.
Main Methods:
- Training NNPs on ab initio simulations, incorporating inhomogeneous configurations from MD simulations with external potentials.
- Applying trained NNPs to predict properties of liquid water and molten NaCl at interfaces.
Main Results:
- NNPs trained on inhomogeneous data significantly improve predictions of density response, surface tension, and cavitation free energies.
- Demonstrated superior accuracy compared to empirical potentials and NNPs lacking inhomogeneous training data.
Conclusions:
- Including inhomogeneous configurations in NNP training is essential for accurate interface simulations.
- This work provides a robust, empirical-potential-free framework for simulating inhomogeneous fluids from first principles.
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