Nicephore Bonnet^1*, Nicola Marzari^1*

1 Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

* Corresponding authors emails: nicephore.bonnet@epfl.ch, nicola.marzari@epfl.ch

DOI10.24435/materialscloud:a0-jh [version v1]

Publication date: May 27, 2024

How to cite this record

Nicephore Bonnet, Nicola Marzari, Solvation free energies from machine learning molecular dynamics, Materials Cloud Archive 2024.80 (2024), https://doi.org/10.24435/materialscloud:a0-jh

Description

In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions. The record includes all molecular-dynamics trajectories, energies and forces used to obtain the solvation energies of alkaline and alkaline-earth ions in water, as reported in Table 2 of referenced paper.

Materials Cloud sections using this data

Files

File name	Size	Description
data.zip MD5md5:29b2e63064a167602ffde561df889df6	2.5 GiB	For each system (water cluster, Li, Na, ...), the LAMMPS MD trajectory, energies and forces are provided. See detailed description in README.txt.
README.txt MD5md5:4e3717bad64e1d6f49da299b4007dc72	384 Bytes	README file.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

solvation machine learning molecular dynamics