arXiv subjects

Motoyuki Shiga

Publications and source records attributed to Motoyuki Shiga.

15 recordsLinked to original sources

arXiv2026

Target-Distribution-Guided Cross-Functional Fine-Tuning of Machine-Learning Interatomic Potentials

Cross-functional fine-tuning of machine-learning interatomic potentials (MLIPs) is often treated as a relabeling problem, where configurations generated at one density-functional level are relabeled using a higher-fidelity target functional. However, the resulting training data may be drawn from the wrong equilibrium distribution, because the statistical weights of configurations change across exchange--correlation functionals. Here we address this distribution mismatch using a target-distribution-guided workflow based on self-learning hybrid Monte Carlo (SLHMC), in which trial configurations are proposed by a machine-learning potential and accepted or rejected using target-functional density-functional-theory energies. Using rutile TiO$_2$ as a test system, we fine-tune the MACE-MP-0 foundation potential toward PBE, r$^2$SCAN, and HSE06 target functionals. The resulting adapted potentials reproduce target-anchored nearest-neighbor Ti--O distributions, radial distribution functions, and the NPT cell metrics examined here more accurately than the foundation-model and off-target relabeling controls considered in this work. In particular, HSE06-guided fine-tuning improves structural and thermodynamic properties that are difficult to access with direct hybrid-functional molecular dynamics because of the computational cost of exact exchange. These results indicate that target-distribution coverage is an essential component of cross-functional MLIP transfer, and that accurate target-level labels alone may be insufficient when the configurational distribution is mismatched.

cond-mat.mtrl-sci↗

arXiv2025

Computation of the heat capacity of water from first principles

Water is a unique solvent with many remarkable properties. An example is its exceptionally high heat capacity, which plays an important role in storing and transporting thermal energy, with implications for many processes from regulating the body temperature of living organisms to moderating our climate at the global scale. To elucidate the microscopic origin of the heat capacity of water from first principles, highly accurate computer simulations are required. Apart from a reliable description of the atomic interactions, the presence of light hydrogen atoms necessitates the explicit consideration of nuclear quantum effects through path integral molecular dynamics (PIMD) simulations. The high computational costs of PIMD simulations, which are even further increased by the need for an extensive statistical sampling of energy fluctuations to determine the heat capacity, can be strongly reduced by replacing first principles calculations with machine learning potentials to represent the atomic interactions. In this study, we use high-dimensional neural network potentials (HDNNPs) constructed from density functional theory calculations employing the RPBE-D3 and revPBE0-D3 functionals. To further enhance the computational performance, we introduce a highly efficient PIMD algorithm computing in parallel not only the energies and forces but also the coordinate and thermostat time evolutions. Using this approach, we are able to determine converged data for the heat capacity from a 4 ns simulation employing 128 beads. In particular, for the revPBE0-D3 functional we find excellent agreement with experiment, providing evidence that our approach represents a promising framework for the quantitative understanding of the thermodynamic properties of water and aqueous solutions.

physics.chem-ph↗

arXiv2024

Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water

The introduction of machine learned potentials (MLPs) has greatly expanded the space available for studying Nuclear Quantum Effects computationally with ab initio path integral (PI) accuracy, with the MLPs' promise of an accuracy comparable to that of ab initio at a fraction of the cost. One of the challenges in development of MLPs is the need for a large and diverse training set calculated by ab initio methods. This data set should ideally cover the entire phase space, while not searching this space using ab initio methods, as this would be counterproductive and generally intractable with respect to computational time.In this paper, we present the self-learning PI hybrid Monte Carlo Method using a mixed ab initio and ML potential (SL-PIHMC-MIX), where the mixed potential allows for the study of larger systems and the extension of the original SL-HMC method [Nagai et al., Phys. Rev. B 102, 041124 (2020)] to PI methods and larger systems. While the MLPs generated by this method can be directly applied to run long-time ML-PIMD simulations, we demonstrate that using PIHMC-MIX with the trained MLPs allows for an exact reproduction of the structure obtained from ab initio PIMD. Specifically, we find that the PIHMC-MIX simulations require only 5,000 evaluations of the 32-bead structure, compared to the 100,000 evaluations needed for the ab initio PIMD result.

physics.chem-ph↗

arXiv2024

Inelastic Neutron Scattering of Hydrogen in Palladium Studied by Semiclassical Dynamics

Inelastic neutron scattering (INS) spectra of hydrogen in face-centered cubic palladium have been calculated considering nuclear quantum effects (NQE) at finite temperatures. The calculations were performed using semiclassical Brownian chain molecular dynamics (MD) [Shiga, J. Comput. Chem. 43, 1864 (2022)] and artificial neural network potentials with an accuracy of generalized gradient approximation of density functional theory. The calculated spectra are in good agreement with experimental spectra with respect to the peak positions and intensities corresponding to the fundamental tone and the first overtone of the vibrational excitation of hydrogen atoms. These results differ significantly from those of classical MD, indicating that NQE plays an essential role in the correct estimation of the INS spectrum. Importantly, the NQE acts as a blue-shift of the INS spectrum for hydrogen in the octahedral site, due to strong anharmonic vibrations of hydrogen on the potential surface with even symmetry. The calculated peak shifts associated with Pd lattice distortion were also in agreement with experimental results.

physics.chem-ph↗

arXiv2024

Path Integral Brownian Chain Molecular Dynamics: A Simple Approximation of Quantum Vibrational Dynamics

An approximate approach to quantum vibrational dynamics, "Brownian Chain Molecular Dynamics (BCMD)", is proposed to alleviate the chain resonance and curvature problems in the imaginary time-based path integral (PI) simulation. Here the non-centroid velocity is randomized at each step when solving the equation of motion of path integral molecular dynamics. This leads to a combination of the Newton equation and the overdamped Langevin equation for the centroid and non-centroid variables, respectively. BCMD shares the basic properties of other PI approaches such as centroid and ring polymer molecular dynamics: It gives the correct Kubo-transformed correlation function at short times, conserves the time symmetry, has the correct high-temperature/classical limits, gives exactly the position and velocity autocorrelations of harmonic oscillator systems, and does not have the zero-point leakage problem. Numerical tests were done on simple molecular models and liquid water. On-the-fly ab initio BCMD simulations were performed for the protonated water cluster, H$_5$O$_2^+$, and its isotopologue, D$_5^{}$O$_2^+$.

physics.chem-ph↗

arXiv2023

Nuclear quantum effect on the elasticity of ice VII under pressure: A path-integral molecular dynamics study

We investigate the effect of nuclear quantum effects (NQEs) of hydrogen atoms on the elasticity of ice VII at high pressure and ambient temperature conditions using ab initio path-integral molecular dynamics (PIMD) calculations. We find that the NQEs of hydrogen contributes to the transition of ice VII from a static disordered structure to a dynamically disordered structure at pressures exceeding 40 GPa. This transition is marked by a discontinuous increase of the elastic constants. Comparison of ab initio molecular dynamics and PIMD calculations reveal that NQEs increase the elastic constants of ice by about 20% at 70 GPa and 300 K.

cond-mat.mtrl-sci↗

arXiv2023

Atomic diffusion due to hyperatomic fluctuation for quasicrystals

A quasicrystal is an ordered but non-periodic structure understood as a projection from a higher dimensional periodic structure. Some physical properties of quasicrystals are different from those of conventional solids. An anomalous increase in heat capacity at high temperatures has been discussed for over two decades as a manifestation of a hidden high dimensionality of quasicrystals. A plausible candidate for this origin has been phason, which has excitation modes originating from additional degrees of freedom in the higher-dimensional lattice. However, most theoretical studies on phasons have used toy models. A theoretical study of the heat capacity of realistic quasicrystals or their approximants has yet to be conducted because of the huge computational complexity. To bridge this gap between experiment and theory, we show experiments and molecular simulations on the same material, an Al--Pd--Ru quasicrystal, and its approximants. We show that at high temperatures, aluminum atoms diffuse with discontinuous-like jumps, and the diffusion paths of the aluminum can be understood in terms of jumps corresponding to hyperatomic fluctuations in six-dimensional space. It is concluded that the anomaly in the heat capacity of quasicrystals arises from extra degrees of freedom due to hyperatomic fluctuations that play a role in diffusive Nambu--Goldstone modes.

cond-mat.mtrl-sci↗

arXiv2021

Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica

Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report (Nagai, {\it et al}. Phys. Rev. B 102, 041124(R) (2020)), the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric ensembles to enable general applications for soft materials and liquids with large volume fluctuation. As a demonstration, the isothermal-isobaric SLHMC method was used to study the vibrational structure of liquid silica at temperatures close to the melting point, whereby the slow diffusive motion is beyond the time scale of first-principles molecular dynamics. It was found that the static structure factor thus computed from first-principles agrees quite well with the high-energy X-ray data.

cond-mat.dis-nn↗

arXiv2021

Mean Force Based Temperature Accelerated Sliced Sampling: Efficient Reconstruction of High Dimensional Free Energy Landscapes

Temperature Accelerated Sliced Sampling (TASS) is an efficient method to compute high dimensional free energy landscapes. The original TASS method employs the Weighted Histogram Analysis Method (WHAM) which is an iterative post-processing to reweight and stitch high dimensional probability distributions in sliced windows that are obtained in the presence of restraining biases. The WHAM necessitates that TASS windows lie close to each other for proper overlap of distributions and span the collective variable space of interest. On the other hand, increase in number of TASS windows implies more number of simulations, and thus it affects the efficiency of the method. To overcome this problem, we propose herein a new mean-force (MF) based reweighting scheme called TASS-MF, which enables accurate computation with a fewer number of windows devoid of the WHAM post-processing. Application of the technique is demonstrated for alanine di- and tripeptides in vacuo to compute their two- and four-dimensional free energy landscapes, the latter of which is formidable in conventional umbrella sampling and metadynamics. The landscapes are computed within a kcal/mol accuracy, ensuring a safe usage for broad applications in computational chemistry.

physics.comp-ph↗

arXiv2021

Nuclear Quantum Effects on Autoionization of Water Isotopologues Studied by Ab Initio Path Integral Molecular Dynamics

In this study we investigate the nuclear quantum effects (NQEs) on the acidity constant (pKA) of liquid water isotopologues at the ambient condition by path integral molecular dynamics (PIMD) simulations. We compared simulations using a fully explicit solvent model with a classical polarizable force field, density functional tight binding, and ab initio density functional theory, which correspond to empirical, semiempirical, and ab initio PIMD simulations, respectively. The centroid variable with respect to the proton coordination number of a water molecule was restrained to compute the gradient of the free energy, which measures the reversible work of the proton abstraction for the quantum mechanical system. The free energy curve obtained by thermodynamic integration was used to compute the pKA value based on probabilistic determination. This technique not only reproduces the pKA value of liquid D2O experimentally measured (14.86) but also allows for a theoretical prediction of the pKA values of liquid T2O, aqueous HDO and HTO which are unknown due to its scarcity. It is also shown that the NQEs on the free energy curve can result in a downshift of 4.5 +/- 0.9 pKA units in the case of liquid water, which indicates that the NQEs plays an indispensable role in the absolute determination of pKA. The results of this study can help to inform further extensions into the calculation of the acidity constants of isotope substituted species with high accuracy.

physics.chem-ph↗

arXiv2019

Self-learning Hybrid Monte Carlo: A First-principles Approach

We propose a novel approach called Self-Learning Hybrid Monte Carlo (SLHMC) which is a general method to make use of machine learning potentials to accelerate the statistical sampling of first-principles density-functional-theory (DFT) simulations. The trajectories are generated on an approximate machine learning (ML) potential energy surface. The trajectories are then accepted or rejected by the Metropolis algorithm based on DFT energies. In this way the statistical ensemble is sampled exactly at the DFT level for a given thermodynamic condition. Meanwhile the ML potential is improved on the fly by training to enhance the sampling, whereby the training data set, which is sampled from the exact ensemble, is created automatically. Using the examples of $α$-quartz crystal SiO$_2^{}$ and phonon-mediated unconventional superconductor YNi$_2^{}$B$_2^{}$C systems, we show that SLHMC with artificial neural networks (ANN) is capable of very efficient sampling, while at the same time enabling the optimization of the ANN potential to within meV/atom accuracy. The ANN potential thus obtained is transferable to ANN molecular dynamics simulations to explore dynamics as well as thermodynamics. This makes the SLHMC approach widely applicable for studies on materials in physics and chemistry.

cond-mat.mtrl-sci↗

arXiv2016

A screened automated structural search with semiempirical methods

We developed an interface program between a program suite for an automated search of chemical reaction pathways, GRRM, and a program package of semiempirical methods, MOPAC. A two-step structural search is proposed as an application of this interface program. A screening test is first performed by semiempirical calculations. Subsequently, a reoptimization procedure is done by ab initio or density functional calculations. We apply this approach to ion adsorption on cellulose. The computational efficiency is also shown for a GRRM search. The interface program is suitable for the structural search of large molecular systems for which semiempirical methods are applicable.

physics.chem-ph↗

arXiv2016

On the hierarchical parallelization of ab initio simulations

A hierarchical parallelization has been implemented in a new unified code PIMD-SMASH for ab initio simulation where the replicas and the Born-Oppenheimer forces are parallelized. It is demonstrated that ab initio path integral molecular dynamics simulations can be carried out very efficiently for systems up to a few tens of water molecules. The code was then used to study a Diels-Alder reaction of cyclopentadiene and butenone by ab initio string method. A reduction in the reaction energy barrier is found in the presence of hydrogen-bonded water, in accordance with experiment.

physics.chem-ph↗

arXiv2013

Multiscale enhanced path sampling based on the Onsager-Machlup action: Application to a model polymer

We propose a novel path sampling method based on the Onsager-Machlup (OM) action by generalizing the multiscale enhanced sampling (MSES) technique suggested by Moritsugu and coworkers (J. Chem. Phys. 133, 224105 (2010)). The basic idea of this method is that the system we want to study (for example, some molecular system described by molecular mechanics) is coupled to a coarse-grained (CG) system, which can move more quickly and computed more efficiently than the original system. We simulate this combined system (original + CG system) using (underdamped) Langevin dynamics where different heat baths are coupled to the two systems. When the coupling is strong enough, the original system is guided by the CG system, and able to sample the configuration and path space more efficiency. We need to correct the bias caused by the coupling, however, by employing the Hamiltonian replica exchange where we prepare many path replica with different coupling strengths. As a result, an unbiased path ensemble for the original system can be found in the weakest coupling path ensemble. This strategy is easily implemented because a weight for a path calculated by the OM action is formally the same as the Boltzmann weight if we properly define the path "Hamiltonian". We apply this method to a model polymer with Asakura-Oosawa interaction, and compare the results with the conventional transition path sampling method.

physics.bio-ph↗

arXiv2010

Onsager-Machlup action-based path sampling and its combination with replica exchange for diffusive and multiple pathways

For sampling multiple pathways in a rugged energy landscape, we propose a novel action-based path sampling method using the Onsager-Machlup action functional. Inspired by the Fourier-path integral simulation of a quantum mechanical system, a path in Cartesian space is transformed into that in Fourier space, and an overdamped Langevin equation is derived for the Fourier components to achieve a canonical ensemble of the path at a finite temperature. To avoid "path trapping" around an initially guessed path, the path sampling method is further combined with a powerful sampling technique, the replica exchange method. The principle and algorithm of our method is numerically demonstrated for a model two-dimensional system with a bifurcated potential landscape. The results are compared with those of conventional transition path sampling and the equilibrium theory, and the error due to path discretization is also discussed.

physics.bio-ph↗