ArticleThe journal of physical chemistry letters2026
Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures at
Article in The journal of physical chemistry letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models.The journal of physical chemistry letters · 2026Article
- Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.Journal of chemical theory and computation · 2026Article
- Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models.Journal of chemical theory and computation · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Large-Scale Cooperative Sulfur Vacancy Dynamics in Two-Dimensional MoSSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-
Identifiers
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Registered trials
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.