Solvation free energies from machine learning molecular dynamics

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Bonnet, Nicephore</dc:creator>
  <dc:creator>Marzari, Nicola</dc:creator>
  <dc: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.</dc:description>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>molecular dynamics</dc:subject>
  <dc:title>Solvation free energies from machine learning molecular dynamics</dc:title>