Abstract

Metastable states and their minimum energy pathways (MEPs) are central to understanding transformations and phase stability in complex materials, yet mapping transition pathways between competing states remains computationally demanding and experimentally challenging. Here, a hybrid solid-state nudged elastic band (SSNEB) framework integrates two pretrained machine learning models, EquiformerV₂ (eqV₂) and the equivariant Smooth Energy Network (eSEN), with DFT for energy, force, and stress evaluations. Applied to CsPbI3, GaN, and TiO2, the framework achieves up to a 7-fold speedup while converging to the same pathways predicted by first-principles calculations.

Abstract

Metastable states and their minimum energy pathways (MEPs) are central to understanding transformations and phase stability in complex materials, yet mapping transition pathways between competing states remains computationally demanding and experimentally challenging. Here, a hybrid solid-state nudged elastic band (SSNEB) framework integrates two pretrained machine learning models, EquiformerV₂ (eqV₂) and the equivariant Smooth Energy Network (eSEN), with DFT for energy, force, and stress evaluations. Applied to CsPbI3, GaN, and TiO2, the framework achieves up to a 7-fold speedup while converging to the same pathways predicted by first-principles calculations.

Abstract

Metastable states and their minimum energy pathways (MEPs) are central to understanding transformations and phase stability in complex materials, yet mapping transition pathways between competing states remains computationally demanding and experimentally challenging. Here, a hybrid solid-state nudged elastic band (SSNEB) framework integrates two pretrained machine learning models, EquiformerV₂ (eqV₂) and the equivariant Smooth Energy Network (eSEN), with DFT for energy, force, and stress evaluations. Applied to CsPbI3, GaN, and TiO2, the framework achieves up to a 7-fold speedup while converging to the same pathways predicted by first-principles calculations.

Abstract

Metastable states and their minimum energy pathways (MEPs) are central to understanding transformations and phase stability in complex materials, yet mapping transition pathways between competing states remains computationally demanding and experimentally challenging. Here, a hybrid solid-state nudged elastic band (SSNEB) framework integrates two pretrained machine learning models, EquiformerV₂ (eqV₂) and the equivariant Smooth Energy Network (eSEN), with DFT for energy, force, and stress evaluations. Applied to CsPbI3, GaN, and TiO2, the framework achieves up to a 7-fold speedup while converging to the same pathways predicted by first-principles calculations.