arXiv subjects

Hodaka Mori

Publications and source records attributed to Hodaka Mori.

4 recordsLinked to original sources

arXiv2026

CoTAR: Topology and Atomic State Reconstruction in Condensed Phases

Universal machine learning interatomic potentials (uMLIPs) enable condensed-phase molecular dynamics (MD) simulations with near-first-principles accuracy, but their lack of explicit molecular topology limits bond-aware analysis and reconnection to classical force fields. Here, we present CoTAR, a hybrid graph neural network (GNN)--hidden Markov model (HMM) framework that reconstructs molecular topology, formal charges, and unpaired electrons from atomic species, coordinates, and total charge by combining message passing on a proximity graph with a van der Waals prior, chemical constraints, and temporal smoothing. Across 128 nonreactive, topology-preserving condensed-phase systems, CoTAR achieved a bond-order-weighted F1 score of 0.906 on classical-MD data; for uMLIP trajectories, few-shot fine-tuning improved the valid-snapshot rate from 38.6\% to 84.7\%. The reconstructed topologies also supported downstream classical MD simulations, and HMM smoothing improved system-level MD simulation feasibility from 83.6\% to 85.9\%, indicating that CoTAR provides a practical framework for bond-aware analysis of condensed-phase uMLIP trajectories.

cond-mat.mtrl-sci↗

arXiv2026

PFP/MM: A Hybrid Approach Combining a Universal Neural Network Potential with Classical Force Fields for Large-Scale Reactive Simulations

Universal machine-learning interatomic potentials (uMLIPs) enable reactive molecular simulations with near-DFT accuracy, yet applying them efficiently to large, realistic condensed-phase systems remains computationally demanding. Here we present PFP/MM, a hybrid approach that combines a uMLIP, PreFerred Potential (PFP), with molecular mechanics (MM) to enable both large-scale and long-time simulations that are challenging for uMLIP-only calculations. Using an alanine dipeptide in explicit water, we achieve multi-ns/day enhanced sampling and obtain a Ramachandran plot consistent with established basins. For an intramolecular nucleophilic addition reaction in a polar solvent environment, we reproduce the expected solvent-induced stabilization in the free-energy profile. We further apply the approach to a cytochrome P450 Compound I hydroxylation reaction and obtain a free-energy landscape consistent with the accepted reaction mechanism. These results demonstrate that uMLIP-based reactive simulations can be applied to diverse condensed-phase processes in large, realistic environments.

cond-mat.mtrl-sci↗

arXiv2025

PARC: An Autonomous Self-Reflective Coding Agent for Robust Execution of Long-Horizon Tasks

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 incorporating task planning, execution, and a mechanism that evaluates its own actions and their outcomes from an independent context and provides feedback, namely self-assessment and self-feedback. This design enables PARC to detect and correct high-level strategic errors and sustain progress without human intervention. We evaluate PARC across computational science and data science tasks. In materials science, it autonomously reproduces key results from studies on lithium-ion conduction and alloy segregation. In particular, it coordinates dozens of parallel simulation tasks, each requiring roughly 43 hours of computation, managing orchestration, monitoring, and error correction end-to-end. In Kaggle-based experiments, starting from minimal natural-language instructions, PARC conducts data analysis and implements search strategies, producing solutions competitive with human-engineered baselines. These results highlight the potential of integrating a hierarchical multi-agent system with self-assessment and self-feedback to enable AI systems capable of independent, large-scale scientific and analytical work.

cs.AI↗

arXiv2025

Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design

Although polymerization and curing reactions govern the performance of advanced materials, their simulation remains challenging owing to the need for accurate, transferable potentials and rarity of chemical events. Conventional reactive force fields such as ReaxFF require system-specific parametrization, while universal machine learning interatomic potentials (uMLIPs) exhibit limited sampling efficiency. This paper introduces a novel simulation framework integrating a uMLIP with a time-dependent bond-boost scheme. The bias potential increases monotonically with time, and the use of a unified parameter set across reaction classes enables consistent acceleration without system-specific tuning. For radical polymerization of vinyl monomers, the proposed framework reproduces characteristic trends, such as linear molecular-weight growth with conversion, initiator-concentration scaling, and relative monomer reactivity trends. For step-growth polycondensation of nylon-6,6, it captures the characteristic sharp increase in molecular weight at high conversion rates, consistent with experimental behavior. For epoxy curing at a copper substrate, it reveals interfacial ring-opening and cross-linking events, consistent with spectroscopic evidence of Cu-O-C bond formation. Overall, coupling uMLIPs with time-dependent bond boost enables practical and transferable simulations of polymerization and curing processes. The proposed framework reliably resolves mechanistic pathways and relative reactivity, offering molecular-level insights into polymer growth and interfacial adhesion.

cond-mat.mtrl-sci↗