ArticleNature communications2025
Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.Journal of chemical theory and computation · 2026Article
- Navigating polymorph generation and distilled-potential development via entropy-symmetry landscapes for metal plasticity mechanisms.Nature communications · 2026Article
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- Few-Shot Ensemble Learning for Catalysis and Application to Trimetallics for Oxygen Reduction.ACS catalysis · 2026Article
- Evidential deep learning for interatomic potentials.Nature communications · 2025Article
- Data-driven dynamic modeling for inverter-based resources using neural networks.Nature communications · 2025Article
- Organic Fusion of Molecular Simulation and Wet-Lab Validation: A Promising High-Throughput Strategy for Screening Bioactive Food Peptides.Foods (Basel, Switzerland) · 2025Review
- Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data.Nature communications · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data causes deterioration of the test performance of MLIPs, and even leads to collapse of MD simulations. In this work, we propose an online Test-time Adaptation Interatomic Potential (TAIP) framework to improve the generalization on test data. Specifically, we design a dual-level self-supervised learning approach that leverages global structure and atomic local environment information to align the model with the test data. Extensive experiments demonstrate TAIP's capability to bridge the domain gap between training and test dataset without additional data. TAIP enhances the test performance on various benchmarks, from small molecule datasets to complex periodic molecular systems with various types of elements. TAIP also enables stable MD simulations where the corresponding baseline models collapse.
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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.