Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials

Authors

Kipton Barros (ORCID: https://orcid.org/0000-0002-1333-5972)
Justin S. Smith (ORCID: https://orcid.org/0000-0001-7314-7896)
Nicholas Lubbers (ORCID: https://orcid.org/0000-0002-9001-9973)
Alice E. A. Allen (ORCID: https://orcid.org/0000-0002-8727-8333)
Aleksandra Pachalieva (ORCID: https://orcid.org/0000-0003-1246-0410)
Galen T. Craven (ORCID: https://orcid.org/0000-0001-5117-2345)
Richard A. Messerly (ORCID: https://orcid.org/0000-0003-1321-4246)
Sergei Tretiak (ORCID: https://orcid.org/0000-0001-5547-3647)
Ying Wai Li (ORCID: https://orcid.org/0000-0003-0124-8262)
Benjamin Nebgen (ORCID: https://orcid.org/0000-0001-5310-3263)
Sakib Matin (ORCID: https://orcid.org/0000-0001-5166-2838)
Emily Shinkle (ORCID: https://orcid.org/0000-0003-2255-0540)

Institutions

Los Alamos National Laboratory (US)
Max Planck Institute for Polymer Research (DE)
Center for Integrated Nanotechnologies (US)
Nvidia (United States) (US)

Publication Details

Journal: Digital Discovery
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5dd00085h
Citations: 3
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 1.55

Funders

U.S. Department of Energy
Office of Science
Basic Energy Sciences
Laboratory Directed Research and Development
Chemical Sciences, Geosciences, and Biosciences Division
Los Alamos National Laboratory

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials

Kipton Barros, Justin S. Smith, Nicholas Lubbers, Alice E. A. Allen et al.

Jan 1, 2025

3 citations

Digital Discovery

Machine Learning in Materials Science

1.55

article

Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials

Kipton Barros, Justin S. Smith, Nicholas Lubbers, Alice E. A. Allen, Aleksandra Pachalieva, Galen T. Craven, Richard A. Messerly, Sergei Tretiak, Ying Wai Li, Benjamin Nebgen, Sakib Matin, Emily Shinkle

January 1, 2025article en

3 citations

Abstract

No abstract available for this paper.

Digital DiscoveryVol. 4(9)

Los Alamos National Laboratory (US), Max Planck Institute for Polymer Research (DE), Center for Integrated Nanotechnologies (US), Nvidia (United States) (US)

U.S. Department of Energy, Office of Science, Basic Energy Sciences, Laboratory Directed Research and Development, Chemical Sciences, Geosciences, and Biosciences Division, Los Alamos National Laboratory

Openalex Percentile: Top 17%

Machine Learning in Materials Science

1.55

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.