Vladimir Ladygin

I’m a materials science researcher at Caltech EAS, where I study fundamental thermal properties in materials at Fultz Group. My work sits at the intersection of theory, experiment, and computation using tools and techniques, including high-performance computing, machine learning and bayesian analyses applied to construction of lattice dynamics models and analysis of inelastic neutron scattering experiments for single crystals.

I eacned my BSc from Moscow Insitute of Physics and Technology in 2018 and continued with two Master Degrees from Moscow Institute of Physics and Technology and Skolokovo Institute of Science and Technology in 2020. During that time I worked with Alexey Yanilkin on Machine Learning Interatomic Potentials Benchmarking and in Alexander Shapeev’s Group on Automatic Gaussian Process based approach to matials phase diagram construction.

During My BSs and MSc I had internships at Krasheninnikov Group at HZDR and Vartaniant Group at DESY

Selected publications

Phonon second harmonic generation in NaBr studied by inelastic neutron scattering and computer simulation

Ladygin, Vladimir, Saunders, Claire, Bernal-Choban, Camille, Abernathy, Douglas, Manley, Michael, and Fultz, Brent

Physical Review Materials 2025

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The phenomenon of second harmonic generation (SHG) was found for phonons in anharmonic NaBr by inelastic neutron scattering. The temperature dependence of this phonon SHG was measured from 300 K to 650 K. At 300 K the second harmonic (SH) is seen as a high-energy branch around 33 meV, nearly independent of Q. The temperature effective potential (TDEP) method and classical molecular dynamics (MD) simulation with machine learning interatomic potential were able to reproduce the SH, and showed that SHG occurs with the flat transverse optical (TO) phonon branch. A classical model of a nonlinear medium explains the intensity and lifetime of the SH, compared to those of the TO modes. Also successful was a quantum model based on the Heisenberg-Langevin equation for interacting phonons coupled to a thermal bath, which also predicts a spectral distribution of the SH. The measured temperature dependence of the intensity of the second harmonic showed that it follows the Planck distribution of a one-phonon quasiparticle, and not two TO phonons.
Atomistic origin of the entropy of melting from inelastic neutron scattering and machine learned molecular dynamics

Bernal-Choban, Camille, Ladygin, Vladimir, Granroth, Garrett, Saunders, Claire, Lohaus, Stefan, Abernathy, Douglas, and Fultz, Brent

Communication Materials 2024

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The latent heat, L, is central to melting, but its atomic origin remains elusive. It is proportional to the entropy of fusion, ΔSfus = L/Tm (Tm is the melting temperature), which depends on changes of atom configurations, atom vibrations, and thermal electron excitations. Here, we combine inelastic neutron scattering and machine-learned molecular dynamics to separate ΔSfus into these components for Ge, Si, Bi, Sn, Pb, and Li. When the vibrational entropy of melting, ΔSvib, is zero, ΔSfus ≃ 1.2 kB per atom. This result provides a baseline for ΔSconfig and nearly coincides with “Richard’s Rule” of melting. The ΔSfus deviates from this value for most elements, however, and we show that this deviation originates with extra ΔSvib and extra ΔSconfig. These two components are correlated for positive and negative deviations from Richard’s rule – the extra ΔSconfig is consistently 80% of ΔSvib. Our results, interpreted with potential energy landscape theory, imply a correlation between the change in the number of basins and the change in the inverse of their curvature for the melting of pure elements.
Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

Ladygin, Vladimir, Beniya, Ilya, Makarov, Edgar, and Shapeev, Alexander

Physical Review B 2021

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Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free-energy functions using data of various origins. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on a Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with the existing studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.
Lattice dynamics simulation using machine learning interatomic potentials

Ladygin, Vladimir, Korotaev, Yuriy, Yanilkin, Alexey, and Shapeev, Alexander

Computational Materials Science 2020

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In this work, a machine-learning approach was applied to obtaining an interatomic potential for lattice dynamics properties calculation with accuracy close to the one of density functional theory (DFT). The computational efficiency of the potential allows one to access large time and length scales through molecular dynamics simulations. The use of active learning and an automatic training procedure greatly reduces the number of quantum-mechanical calculations for the training set. In order to estimate the accuracy of the obtained potentials, four materials Al, Mo, Ti and U with different phonon and thermodynamic properties were investigated. Phonon properties were calculated using the temperature dependent effective potential method. The potentials reproduce not only harmonic behavior but also anharmonic effects, as shown by the calculation of the third-order force constants. We found that machine-learning potentials reproduce quantum-mechanical data with high accuracy. Furthermore, the vibrational density of states was obtained via velocity autocorrelation function integration, which would be infeasible in direct quantum-mechanical simulations.