ArticleNature communications2025
An automated framework for exploring and learning potential-energy surfaces.
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 12 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
12 citing papers in PubMed.
- Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis.JACS Au · 2026Review
- Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks.Journal of the American Chemical Society · 2026Article
- Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Medium-Range Structural Order in Amorphous Arsenic.Journal of the American Chemical Society · 2026Article
- Chemistry in Extreme Environments: The Mystery of Molecular Complexity in Space.ACS central science · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling.npj computational materials · 2026Article
- A python workflow definition for computational materials design.Digital discovery · 2025Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential.Nature communications · 2025Article
- Atomate2: modular workflows for materials science.Digital discovery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex ('automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO
Identifiers
What OpenQuestion holds
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.