aceMachine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications
Machine learning force fields (MLFFs) combine the high accuracy of first-principles methods with the high efficiency of classical force fields, offering new opportunities for atomic-level studies of inorganic crystalline materials. We systematically summarize the research progress on MLFFs, elucidate their fundamental principles and developmental history, and categorically introduce the technical characteristics of representative models and relevant benchmarking platforms. We aim to review the advantages of MLFF…
MLFFs provide high accuracy with high efficiency for atomic-level studies of inorganic crystalline materials, overcoming traditional limits in structure prediction, properties, defects, and phase transitions.
MLFFs still face challenges in computational efficiency and scale, accuracy and generalization, data requirements, interpretability, and physical constraints when applied to inorganic crystalline materials.
Challenges remain in computational efficiency and simulation scale, accuracy and generalization ability, data requirements and training samples, model interpretability, and physical constraints for inorganic crystalline materials.
Evidence
- Peer-reviewedPhysical Chemistry Chemical Physics2026-07-17
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Truvace Impact Record TRV-2026-0243, v1: “Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications.” Truvace, 2026-07-17. /record/TRV-2026-0243 (accessed at citation time). sha256 1fe7174d819bcc0c…
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