aceSelf-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design
Abstract Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled…
Self-optimizing attention-coupled neural network potential automates crystal structure prediction and iteratively refines itself, enabling exploration of nearly 10 million configurations with ab initio accuracy and substantial speedup over first-principles calculations.
Evidence
- Peer-reviewednpj Computational Materials2026-01-26
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Truvace Impact Record TRV-2026-0407, v1: “Self-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design.” Truvace, 2026-07-20. /record/TRV-2026-0407 (accessed at citation time). sha256 3fe69480d0b939b5…
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