Showing cond-mat.mtrl-sciShow all
3 papers · 1 filter
cond-mat.mtrl-sci2026
CoTAR: Topology and Atomic State Reconstruction in Condensed Phases
Hodaka Mori, Yu Miyazaki, Takechika Kikkawa
Universal machine learning interatomic potentials (uMLIPs) enable condensed-phase molecular dynamics (MD) simulations with near-first-principles accuracy, but their lack of explici…
cond-mat.mtrl-sci2026
PFP/MM: A Hybrid Approach Combining a Universal Neural Network Potential with Classical Force Fields for Large-Scale Reactive Simulations
Yu Miyazaki, Atsuhiro Tomita, Akihide Hayashi +3
Universal machine-learning interatomic potentials (uMLIPs) enable reactive molecular simulations with near-DFT accuracy, yet applying them efficiently to large, realistic condensed…
cond-mat.mtrl-sci2025
Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design
Hodaka Mori, Shunsuke Tonogai, Yu Miyazaki +2
Although polymerization and curing reactions govern the performance of advanced materials, their simulation remains challenging owing to the need for accurate, transferable potenti…