arXiv subjects

Yu Miyazaki

Publications and source records attributed to Yu Miyazaki.

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

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↗

arXiv2023

Equivariant Neural Networks for Spin Dynamics Simulations of Itinerant Magnets

I present a novel equivariant neural network architecture for the large-scale spin dynamics simulation of the Kondo lattice model. This neural network mainly consists of tensor-product-based convolution layers and ensures two equivariances: translations of the lattice and rotations of the spins. I implement equivariant neural networks for two Kondo lattice models on two-dimensional square and triangular lattices, and perform training and validation. In the equivariant model for the square lattice, the validation error (based on root mean squared error) is reduced to less than one-third compared to a model using invariant descriptors as inputs. Furthermore, I demonstrate the ability to reproduce phase transitions of skyrmion crystals in the triangular lattice, by performing dynamics simulations using the trained model.

cond-mat.str-el↗