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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Machine Learning Interatomic Potentials and Long-Range Physics.

Dylan M Anstine¹, Olexandr Isayev¹

¹Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

The Journal of Physical Chemistry. A

|February 21, 2023

Summary

This summary is machine-generated.

Machine learned interatomic potentials (MLIPs) are advancing rapidly. This work discusses methods to improve MLIPs by incorporating nonlocal physics for accurate simulations of complex systems.

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Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

Machine learned interatomic potentials (MLIPs) offer near ab initio accuracy at reduced computational cost.
Accurate modeling of complex systems (macromolecules, biomolecules, condensed matter) requires incorporating both short- and long-range interactions.
Traditional MLIPs often struggle with accurately describing nonlocal physical and chemical interactions.

Purpose of the Study:

To present a perspective on key methodologies for developing MLIPs that account for nonlocal physics.
To highlight strategies for improving MLIP accuracy in systems where nonlocal effects are crucial.
To guide the development of advanced MLIPs for diverse scientific applications.

Main Methods:

Augmenting MLIPs with dispersion corrections.
Calculating electrostatics using charges derived from atomic environment descriptors.
Employing self-consistency and message-passing iterations to propagate nonlocal information.
Utilizing charge equilibration schemes for improved accuracy.

Main Results:

Demonstrated strategies for incorporating nonlocal electrostatic and dispersion interactions into MLIPs.
Showcased the broad applicability of MLIPs enhanced with nonlocal physics.
Provided a framework for developing MLIPs that overcome limitations of nearsightedness.

Conclusions:

Accurate MLIPs for complex systems necessitate the inclusion of nonlocal interactions.
Advanced methodologies enable MLIPs to capture crucial nonlocal physics and chemistry.
This perspective supports the creation of more robust and versatile machine learning potentials.