Computing energy changes in chemical reactions more efficiently

Open-source framework package constructs accurate and computationally efficient MLIPs without relying on an extensive prelabeled dataset

Authors

Publication Details

Journal
Scilight
Published
2026-11-02
DOI
https://doi.org/10.1063/10.0047513
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Computing energy changes in chemical reactions more efficiently

Jennifer Shim
4 links
Scilight
Machine Learning in Materials Science
article

Computing energy changes in chemical reactions more efficiently

Jennifer Shim
article en

Abstract

Open-source framework package constructs accurate and computationally efficient MLIPs without relying on an extensive prelabeled dataset

ScilightVol. 2026(45)
Openalex Percentile: Top 31%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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Computing energy changes in chemical reactions more efficiently — Jennifer Shim · Scilight (2026) | TGRS Research Map | TGRS