General-purpose machine-learned potential for 16 elemental metals and their alloys
University of Science and Technology Beijing · Hunan University · +15 more institutions
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,…
Citation impact
- FWCI
- 16.58
- Percentile
- 100%
- References
- 89
Authors
28- KSKeke Song
University of Science and Technology Beijing
- RZRui Zhao
Hunan University
- JLJiahui Liu
University of Science and Technology Beijing
- YWYanzhou Wang
Collaborative Innovation Center of Advanced Microstructures, Nanjing University, University of Science and Technology Beijing, Aalto University
- ELEric Lindgren
Chalmers University of Technology
Topics & keywords
- Computer science