arXiv subjects

Seiji Ueno

Publications and source records attributed to Seiji Ueno.

3 recordsLinked to original sources

arXiv2026

sbml4md: A computational platform for System-Bath Modeling via Molecular Dynamics powered by Machine Learning

We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian (MAB) models from molecular dynamics (MD) trajectories for simulating nonlinear vibrational spectra of intramolecular modes of molecular liquids. By leveraging machine learning (ML) techniques to capture vibrational anharmonicity, intermolecular couplings, and bath correlation functions for each mode, sbml4md obviates empirical fitting and enables the modeling of environments with spatial and temporal heterogeneity. This work provides a set of parameters specifically tailored for the Hierarchical Equations of Motion (HEOM) framework, enabling numerically "exact" simulations of nonlinear vibrational spectra. Building upon our previous implementation for intramolecular vibrational modes [Park, Jo, and Tanimura, J. Chem. Phys. 163, 214104 (2025)], the present code enhances optimization efficiency by explicitly accounting for intermolecular vibrational contributions. This extension enables sbml4md to broaden the applicability of HEOM-based dynamical modeling by seamlessly integrating classical MD approaches, thereby providing a flexible and scalable framework for simulating both linear and nonlinear spectra under realistic conditions with minimal empirical input. The accompanying ML code, written in Python, is provided as supporting material.

physics.chem-ph↗

arXiv2021

Modeling and simulating the excited-state dynamics of a system with condensed phases: A machine learning approach

Simulating the irreversible quantum dynamics of exciton and electron transfer problems poses a nontrivial challenge. Because the irreversibility of the system dynamics is a result of quantum thermal activation and dissipation caused by the surrounding environment, it is necessary to include infinite environmental degrees of freedom in the simulation. Because the capabilities of full quantum dynamics simulations that include the surrounding molecular degrees of freedom are limited, employing a system-bath model is a practical approach. In such a model, the dynamics of excitons or electrons are described by a system Hamiltonian, while the other degrees of freedom that arise from the environmental molecules are described by a harmonic oscillator bath (HOB) and system-bath interaction parameters. By extending on a previous study of molecular liquids [J. Chem. Theory Comput. 2020, 16, 2099], here we construct a system-bath model for exciton and electron transfer problems by means of a machine learning approach. We determine both the system and system-bath interaction parameters, including the spectral distribution of the bath, using the electronic excitation energies obtained from a quantum mechanics/molecular mechanics (QM/MM) simulation that is conducted as a function of time. Using the analytical expressions of optical response functions, we calculate linear and two-dimensional electronic (2DES) spectra for indocarbocyanine dimers in methanol. From these results, we demonstrate the capability of our approach to elucidate the nonequilibrium exciton dynamics of a quantum system in a nonintuitive manner.

physics.chem-ph↗

arXiv2020

Modeling intermolecular and intramolecular modes of liquid water using multiple heat baths: Machine learning approach

The vibrational motion of molecules in dissipative environments, such as solvation and protein molecules, is composed of contributions from both intermolecular and intramolecular modes. The existence of these collective modes introduces difficulty into quantum simulations of chemical and biological processes. In order to describe the complex molecular motion of the environment in a simple manner, we introduce a system-bath model in which the intramolecular modes with anharmonic mode-mode couplings are described by a system Hamiltonian, while the other degrees of freedom, arising from the environmental molecules, are described by heat bath. Employing a machine-learning based approach, we determine not only the system parameters of the intramolecular modes but also the spectral distribution of the system-bath coupling to describe the intermolecular modes, using the atomic trajectories obtained from molecular dynamics (MD) simulations. The capabilities of the present approach are demonstrated for liquid water using MD trajectories calculated from the SPC/E model and the polarizable water model for intramolecular and intermolecular vibrational spectroscopies (POLI2VS) by determining the system parameters describing the symmetric-stretch, asymmetric-stretch and bend modes with intramolecular interactions and the bath spectral distribution functions for each intramolecular mode representing the interaction with the intra-molecular modes. From these results, we were able to elucidate the energy relaxation pathway between the intramolecular modes and the intermolecular modes in a non-intuitive manner.

cond-mat.soft↗