Using graph neural network and symbolic regression to model disordered systems.

Ruoxia Chen, Mathieu Bauchy, Wei Wang, Yizhou Sun, Xiaojie Tao, Jaime Marian

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Ruoxia ChenPhysics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA. ruoxia@g.ucla.edu.

Mathieu BauchyPhysics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA.

Wei WangDepartment of Computer Science, University of California, Los Angeles, CA, 90095, USA.

Yizhou SunDepartment of Computer Science, University of California, Los Angeles, CA, 90095, USA.

Xiaojie TaoDepartment of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA.

Jaime MarianDepartment of Materials Science and Engineering, University of California, Los Angeles, CA, 90095, USA. jmarian@g.ucla.edu.

Funding

National Science Foundation 1944510

6 · The paper itself

Abstract

The key to modeling disordered systems lies in accurately simulating atomic trajectories, typically achieved through molecular dynamic (MD) simulation. The accuracy of MD simulations depends on the precision of the interatomic potential function, which dictates the calculations of atom movements. Traditionally, deriving interatomic potential function relies on extensive prior physical knowledge and high computational cost. This study introduces a novel approach that integrates machine learning with molecular dynamic methods to provide precise interatomic potential energy calculations for disordered systems.

Indexed as

Force fieldMachine learningMolecular dynamic simulations

Identifiers

PMID40594057

DOI10.1038/s41598-025-05205-8

PMCPMC12218424

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.