3 papers
physics.comp-ph2026
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9
First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning…
cond-mat.mtrl-sci2025
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid solid electrolytes CsHPO and CsHSO, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion…
cond-mat.soft2024
Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions
Huan Tran, Chiho Kim, Rishi Gurnani +8
Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods…