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emle-engine: A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics

Kirill Zinovjev1, Lester Hedges2,3, Rubén Montagud Andreu1

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Journal of Chemical Theory and Computation
|May 28, 2024
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Summary
This summary is machine-generated.

We introduce emle-engine, a new machine learning embedding scheme for molecular dynamics simulations. This electrostatic machine learning embedding (EMLE) model improves accuracy over traditional methods for systems with changing charge distributions.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics Simulations
  • Machine Learning in Chemistry

Background:

  • Hybrid machine learning potential/molecular-mechanics (ML/MM) simulations are crucial for modeling complex chemical systems.
  • Accurate representation of electronic charge distribution and induction effects is essential for reliable simulation results.
  • Existing methods often struggle with systems exhibiting significant charge variations in the machine learning subsystem or environment.

Purpose of the Study:

  • To present the emle-engine package, implementing a novel electrostatic machine learning embedding (EMLE) scheme for ML/MM dynamics.
  • To evaluate the performance and stability of the EMLE scheme in enhanced sampling molecular dynamics simulations.
  • To demonstrate the superiority of EMLE compared to traditional molecular mechanics (MM) embedding for accurate free energy calculations.

Main Methods:

  • Developed the emle-engine package based on a physics-informed model of electronic density and induction.
  • The EMLE scheme utilizes tunable parameters derived from in vacuo properties and requires only atomic positions and partial charges.
  • Tested EMLE by calculating free energy surfaces of alanine dipeptide in water using various ML potentials and embedding models.

Main Results:

  • The EMLE embedding scheme demonstrated stability in enhanced sampling molecular dynamics simulations.
  • EMLE significantly outperformed traditional MM embedding with fixed partial charges when compared to DFT/MM reference calculations.
  • The inclusion of configurational electronic density dependence and induction energy in EMLE led to a systematic reduction in free energy surface errors.

Conclusions:

  • The emle-engine package provides a robust and accurate electrostatic embedding scheme for ML/MM simulations.
  • EMLE enables accurate modeling of systems with dynamic charge distributions, advancing the capabilities of computational chemistry.
  • This work facilitates the application of advanced ML/MM techniques to complex chemical and biological processes.