General-purpose machine-learned potential for 16 elemental metals and their alloys
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...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54554-x |
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| Summary: | 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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| ISSN: | 2041-1723 |