Solvation free energies from machine learning molecular dynamics

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 950, 
    "_oai": {
      "id": "oai:materialscloud.org:2061"
    }, 
    "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 (\u2248200 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.\nThe 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.", 
    "mcid": "2024.80", 
    "id": "2061", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "references": [
      {
        "citation": "N. Bonnet, N. Marzari, J. Chem. Theory Comput. (2024)", 
        "type": "Journal reference", 
        "url": "https://pubs.acs.org/doi/10.1021/acs.jctc.4c00116", 
        "doi": "10.1021/acs.jctc.4c00116"
      }
    ], 
    "doi": "10.24435/materialscloud:a0-jh", 
    "keywords": [
      "solvation", 
      "machine learning", 
      "molecular dynamics"
    ], 
    "contributors": [
      {
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Bonnet", 
        "email": "nicephore.bonnet@epfl.ch", 
        "givennames": "Nicephore"
      }, 
      {
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Marzari", 
        "email": "nicola.marzari@epfl.ch", 
        "givennames": "Nicola"
      }
    ], 
    "conceptrecid": "2060", 
    "version": 1, 
    "publication_date": "May 27, 2024, 10:10:43", 
    "is_last": true, 
    "status": "published", 
    "_files": [
      {
        "size": 2682644461, 
        "checksum": "md5:29b2e63064a167602ffde561df889df6", 
        "description": "For each system (water cluster, Li, Na, ...), the LAMMPS MD trajectory, energies and forces are provided. See detailed description in README.txt.", 
        "key": "data.zip"
      }, 
      {
        "size": 384, 
        "checksum": "md5:4e3717bad64e1d6f49da299b4007dc72", 
        "description": "README file.", 
        "key": "README.txt"
      }
    ], 
    "title": "Solvation free energies from machine learning molecular dynamics"
  }, 
  "id": "2061", 
  "updated": "2024-05-27T08:10:43.941737+00:00", 
  "created": "2024-01-25T16:56:11.103455+00:00", 
  "revision": 7
}