{
  "abstract": "Enzyme catalysis can be simulated accurately and efficiently by coupling machine-learned potentials trained on gas-phase data to the environment (ML/MM) using electrostatic machine-learning embedding (EMLE).",
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      "name": "Iñaki Tuñón"
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    {
      "name": "Marc W van der Kamp"
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      "name": "Kirill Zinovjev"
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  "bibtex": "https://research.draconitic.com/paper/221mt6re38rp5.bib",
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  "doi": "10.1039/d6sc01156j",
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  "published": "2026-10-18",
  "publisher": "The Royal Society of Chemistry",
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
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  "title": "Simulating enzyme catalysis with electrostatically embedded machine learning potentials",
  "title_text": "Simulating enzyme catalysis with electrostatically embedded machine learning potentials",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:58Z",
  "url": "https://research.draconitic.com/paper/221mt6re38rp5",
  "venue": "Chemical Science",
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}