Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

• Published in: npj computational materials Vol. 11; no. 1; pp. 218 - 17
• Main Authors: P. A. Subramanyam, Aparna, Perez, Danny
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1023/1026; 639/301/1034/1035; 639/301/1034/1037; 639/301/119; Accuracy; Body centered cubic lattice; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Configurations; Datasets; Energy; Entropy; Entropy (Information theory); Face centered cubic lattice; Learning algorithms; Machine learning; Material properties; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Optimization; Simulation; Subject specialists; Theoretical

In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the prop...