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physics.comp-ph2026
dpti: An Automated Thermodynamic Integration Workflow for Phase Diagram Calculations with Machine Learning Interatomic Potentials
Fengbo Yuan, Xin Zhong, Donghao Zheng +4
Thermodynamic integration (TI) is a widely used approach for computing free energies and phase diagrams. However, TI calculations driven by machine learning interatomic potentials…
physics.chem-ph2026
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Tiancheng Li, Wentao Li, Anyang Peng +4
Machine-learning interatomic potentials now approach quantum-mechanical accuracy, but the most expressive equivariant architectures are costly to evaluate, and the leading ones dep…