ArticleNature communications2024
General-purpose machine-learned potential for 16 elemental metals and their alloys.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
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Who cites it
23 citing papers in PubMed.
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- From Experiment-Driven to Theory- and Data-Driven: A Computational Paradigm Shift in High-Entropy Electrocatalyst Design.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Unveiling Mechanisms of SEI Formation and Sodium Loss in Sodium Batteries via Interface Reactor Sampling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computational Simulation of Irradiation-Induced Structural Defects in Metallic Materials: Formation, Evolution, and Mechanical Effects.Nanomaterials (Basel, Switzerland) · 2026Review
- Octahedral tilting in halide double perovskites: disentangling lone-pair chemistry and geometric effects.Faraday discussions · 2026Article
- Machine learning potentials for modeling alloys across compositions.Science advances · 2026Article
- qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic Simulations.Journal of chemical theory and computation · 2026Article
- Enhancing multi-component alloy composition prediction based on generative adversarial networks and proximal policy optimization.Scientific reports · 2026Article
- Research on Multiscale Characterization and Computational Modeling/Simulation of Metallic Materials.Materials (Basel, Switzerland) · 2026Article
- Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.ACS applied materials & interfaces · 2026Review
- Transforming Grain-Boundary Brittle Precipitates to Ductility Pathways in Complex Concentrated Alloy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bridging the Gap: Using Machine Learning Force Fields to Simulate Gold Break Junctions at Pulling Speeds Closer to Experiments.ACS nano · 2025Article
- Revealing the Low-Temperature Phase of FAPbIJournal of the American Chemical Society · 2025Article
- Lattice distortion leads to glassy thermal transport in crystalline CsProceedings of the National Academy of Sciences of the United States of America · 2025Article
- A morphotropic phase boundary in MANature communications · 2025Article
- Atomic armor for thermal stability in nanoporous structures.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Thermal Transport of Amorphous Hafnia Across the Glass Transition.ACS materials letters · 2025Article
- Unveiling the microscopic origin of anomalous thermal conductivity in amorphous carbon.Science advances · 2025Article
- Tough and strong bioinspired high-entropy all-ceramics with a contiguous network structure.Nature communications · 2025Article
- Machine Learning Advances in High-Entropy Alloys: A Mini-Review.Entropy (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
28 authors.
Funding
Abstract
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys.
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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.