ArticleNature communications2025
Evidential deep learning for interatomic potentials.
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 3 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
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Who cites it
3 citing papers in PubMed.
- Transfer Learning from Homogeneous to Heterogeneous: Fine-Tuning a Pretrained Interatomic Potential for Multicomponent Mo Alloys with Localized Substitutional Alloying.Materials (Basel, Switzerland) · 2026Article
- Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Evidential deep learning for interatomic potentials.Nature communications · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning interatomic potentials have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. To ensure the reliability of the simulation, the training dataset is iteratively expanded through active learning, where uncertainty serves as a critical indicator for identifying and collecting out-of-distribution data. However, existing uncertainty quantification methods tend to involve either expensive computations or compromise prediction accuracy. Here we show an evidential deep learning framework for interatomic potentials with a physics-inspired design. Our method provides uncertainty quantification without significant computational overhead or decreased prediction accuracy, consistently outperforming other methods across a variety of datasets. Furthermore, we demonstrate applications in exploring diverse atomic configurations, using examples including water and universal potentials. These results highlight the potential of our method as a robust and efficient alternative for uncertainty quantification in molecular simulations.
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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.