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Roadmap to CCSD(T)-Quality Machine-Learned Potentials for Condensed Phase Simulations.

Eric D Boittier¹, Silvan Käser¹, Markus Meuwly¹

¹Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland.

Journal of Chemical Theory and Computation

|September 12, 2025

Summary

A new machine learning-driven workflow creates efficient energy functions for molecular dynamics (MD) simulations. This approach accurately models water's condensed phase properties, promising advancements in computational chemistry.

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Area of Science:

Computational Chemistry
Materials Science
Chemical Physics

Background:

Accurate energy functions are crucial for molecular dynamics (MD) simulations in condensed phase systems.
Existing methods often face limitations in computational efficiency or accuracy.
Developing efficient and accurate models is essential for advancing molecular simulations.

Purpose of the Study:

To present a generic workflow for creating computationally efficient energy functions.
To combine machine learning (ML) and empirical models for intra- and intermolecular interactions.
To apply and validate this workflow for condensed phase water simulations.

Main Methods:

Decomposition of total energy into internal, electrostatic, and van der Waals contributions.
Utilizing neural networks for monomer potential energy surfaces and flexible minimally distributed charge models for electrostatics.
Fitting remaining energy contributions using Lennard-Jones (LJ) terms and referencing electronic structure calculations (CCSD(T)-F12, DFT).

Main Results:

The workflow was successfully applied to water, with LJ(12-6) parameters optimized using bulk liquid density and heat of vaporization.
MD simulations on the multi-nanosecond timescale accurately reproduced various condensed phase properties of water.
The ML-inspired parametrization scheme demonstrated promising performance compared to experimental data.

Conclusions:

The presented generic workflow offers a promising approach for developing accurate and efficient energy functions for MD simulations.
This ML-driven method shows potential for broad applicability across different condensed phase systems.
Future work can focus on further improvements and extensions, leveraging recent advancements in water modeling.