4 papers
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…
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…
PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks
Yuki Orimo, Iori Kurata, Hodaka Mori +3
We introduce PARC, a coding agent for the autonomous and robust execution of long-horizon computational tasks. PARC is built on a hierarchical multi-agent architecture incorporatin…
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…