Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules

2024/01/01 by Patrizia Mazzeo, Edoardo Cignoni, Amanda Arcidiacono +2 · 1 voice · 2 citations
Materials Science · Physics and Astronomy · #Machine Learning in Materials Science #Spectroscopy and Quantum Chemical Studies

paper · pdf · doi:10.1039/d4dd00295d

openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/21

Abstract

We propose a strategy to perform electrostatic embedding machine learning (ML)/molecular mechanics (MM) molecular dynamics simulations.

Citations

• PLUMED 2: New feathers for an old bird
• Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Gaussian Process Regression for Materials and Molecules
• AmberTools

Cited by

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Discussions

• Want to know more? Have a look at Patrizia's paper in Digital Discovery doi.org/10.1039/D4DD... [bsky, 4 points, 0 comments]

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