arXiv subjects

Bo Thomsen

Publications and source records attributed to Bo Thomsen.

5 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↗

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↗