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Combining the D3 dispersion correction with the neuroevolution machine-learned potential.

Penghua Ying1, Zheyong Fan2

  • 1Department of Physical Chemistry, School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel.

Journal of Physics. Condensed Matter : an Institute of Physics Journal
|December 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the neuroevolution potential with D3 correction (NEP-D3) model to accurately simulate both short- and long-range interactions in materials. The NEP-D3 model improves descriptions of binding energies and reduces thermal conductivity in metal-organic frameworks.

Keywords:
D3 dispersion correctionGPUMDbilayer graphenemachine-learned potentialsmetal-organic frameworksneuroevolution potentialthermal conductivity

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Area of Science:

  • Computational materials science
  • Quantum chemistry
  • Condensed matter physics

Background:

  • Machine-learned potentials (MLPs) are widely used for atomistic simulations.
  • Short cutoffs in MLPs limit the accurate modeling of long-range dispersion interactions.
  • Accurate modeling of dispersion forces is crucial for many material properties.

Purpose of the Study:

  • To develop a unified model combining neuroevolution potential (NEP) with D3 dispersion corrections.
  • To enable simultaneous modeling of short-range bonded and long-range dispersion interactions.
  • To enhance the accuracy of atomistic simulations for materials science applications.

Main Methods:

  • Integration of the D3 dispersion correction scheme into the neuroevolution potential framework, creating the NEP-D3 model.
  • Implementation of the NEP-D3 model within the gpumd package for broad applicability.
  • Validation through simulations of bilayer graphene and metal-organic frameworks.

Main Results:

  • The NEP-D3 model provides improved descriptions of binding and sliding energies in bilayer graphene compared to pure NEP.
  • Dispersion interactions were shown to reduce thermal conductivity by approximately 10% in three metal-organic frameworks.
  • The D3 correction was successfully implemented in gpumd, supporting various exchange-correlation functionals.

Conclusions:

  • The NEP-D3 model effectively captures both short-range and long-range interactions in atomistic simulations.
  • This approach offers a more accurate and versatile tool for materials modeling.
  • The findings have implications for understanding and predicting material properties like thermal conductivity.