11 citations · 17 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid electrolytes CsHPO and CsHSO, the origin of fast proton conduction has long been debated, attributed either to local proton hopping or to polyanion rotati…
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…