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Related Experiment Video

Updated: Sep 29, 2025

Experimental Multiscale Methodology for Predicting Material Fouling Resistance
09:13

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A Hessian-based assessment of atomic forces for training machine learning interatomic potentials.

Marius Herbold1, Jörg Behler1

  • 1Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany.

The Journal of Chemical Physics
|March 23, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new method to identify reliable molecular fragments for training machine learning potentials (MLPs). This approach simplifies creating accurate MLPs for large systems by providing precise atomic forces.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Machine learning potentials (MLPs) offer near first-principles accuracy for high-dimensional potential-energy surfaces (PESs).
  • MLPs often utilize atomic energy contributions based on local chemical environments and atomic forces for training.
  • Calculating forces for large systems is computationally expensive, necessitating efficient training set construction.

Purpose of the Study:

  • To propose a novel method for determining structurally converged molecular fragments.
  • To generate reliable atomic forces from these fragments for training MLPs.
  • To assess the locality and long-range interaction importance in MLPs.

Main Methods:

  • Analysis of the Hessian matrix to determine structurally converged molecular fragments.
  • Utilizing these fragments to obtain reliable atomic forces.
  • Applying the method to molecular model systems and the metal-organic framework MOF-5.

Main Results:

  • Demonstrated a method for selecting optimal molecular fragments for force calculations.
  • Showcased the ability to estimate the importance of long-range interactions.
  • Successfully applied the technique to a complex hybrid material, MOF-5.

Conclusions:

  • The proposed Hessian-based method effectively identifies converged fragments for accurate atomic force generation.
  • This approach simplifies the creation of robust training sets for machine learning potentials.
  • Facilitates the development of accurate MLPs for large and complex materials systems.