11 citations · 14 across the 3 of their papers we have counts for
4 papers
Predicting Interface Structure using the Minima Hopping Method with a Machine Learning Interatomic Potential
Chang-Ti Chou, Menghang Wang, Chao Yang +4
Predicting atomic-scale interfacial structures remains a central challenge in materials science due to their structural complexity and the difficulty of direct comparison between c…
Coupled reaction and diffusion governing interface evolution in solid-state batteries
Jingxuan Ding, Laura Zichi, Matteo Carli +4
Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid…
High-performance training and inference for deep equivariant interatomic potentials
Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak +11
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in a…
Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
Yu Xie, Menghang Wang, Senja Ramakers +2
Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent ve…