Publications

2025

1. 

   Diffuse intensity from phonon noise in inelastic neutron scattering

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

   Physical Review Materials 2025

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   Atomic vibrational dynamics in cuprite Cu2O was studied by inelastic neutron scattering and molecular-dynamics (MD) simulations from 10 K to 900 K. At 300 K, a diffuse inelastic intensity (DII) appeared in the phonon dispersions, and dominated the spectral intensity at higher temperatures. Classical MD simulations with a machine-learning interatomic potential reproduced general features of the DII. Better agreement with experiment was obtained with the addition of a stiffer potential at close approaches of the Cu and O-atoms. The DII originates from random phase shifts of vibrating O atoms that have brief (∼10fs) anharmonic interactions with neighboring Cu atoms. The spectrum of DII gives information about the interaction time of anharmonic interactions between atoms, and its intensity gives a strength of coupling between vibrating atoms and a thermal bath.

2. 

   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.

2024

1. 

   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.

2. Quantifying components of entropy in the latent heat of melting

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

   Bulletin of the American Physical Society 2024

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   The latent heat of melting in materials, L= TmΔSfus, is equal to the melting temperature, Tm, times the entropy of fusion, ΔSfus. While many studies focus on predicting the melting temperature of materials, the empirical Richard’s rule of approximately 1.1 kB/atom is often still used to estimate the entropy of fusion despite calorimetric measurements of ΔSfus spanning nearly an order of magnitude. Here, we combine inelastic neutron scattering and molecular dynamics informed by machine learning interatomic potentials to quantify the vibrational, configurational, and electronic contributions to the entropy of fusion in Ge, Si, Bi, Sn, Pb, and Li. Our results reveal a correlation between the total and vibrational entropies across the melt and, using the framework of potential energy landscape theory, imply that the change in the number of basins and the change in their curvature upon melting are inversely related.

2023

1. Bayesian inference of composition-dependent phase diagrams

   Miryashkin, Timofei, Klimanova, Olga, Ladygin, Vladimir, and Shapeev, Alexander

   Physical Review B 2023

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   Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can manifest under specific conditions. In this work, we develop a method in which Bayesian inference is employed to combine thermodynamic data from molecular dynamics (MD), melting point simulations, and phonon calculations, process these data, and yield a temperature-concentration phase diagram. The employed Bayesian framework yields not only the free energies of different phases as functions of temperature and concentration but also the uncertainties of these free energies originating from statistical errors inherent to finite-length MD trajectories. Furthermore, it extrapolates the results of the finite-atom calculations to the infinite-atom limit and facilitates the choice of temperature, chemical potentials, and the number of atoms conducting the next simulation with which will be the most efficient in reducing the uncertainty of the phase diagram. The developed algorithm was successfully tested on two binary systems, Ge-Si and K-Na, in the full range of concentrations and temperatures.

2. Diffuse Inelastic Neutron Scattering from Anharmonic Vibrations in Cuprite

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

   arXiv preprint arXiv:2309.00780 2023

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   Atomic vibrational dynamics in cuprite, Cu2O, was studied by inelastic neutron scattering and molecular dynamics (MD) simulations from 10 K to 900 K. Above 300 K, a diffuse inelastic intensity (DII) appeared, obscuring the high-energy phonon modes. Classical MD simulations with a machine learning interatomic potential reproduced general features of the DII, especially with a Langevin thermostat. The DII originates from random phase shifts of vibrating O-atoms that have anharmonic interactions with neighboring Cu-atoms.

2022

2021

1. 

   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.

2020

1. Freestanding and supported MoS2 monolayers under cluster irradiation: insights from molecular dynamics simulations

   Ghaderzadeh, Sadegh, Ladygin, Vladimir, Ghorbani-Asl, Mahdi, Hlawacek, Gregor, Schleberger, Marika, and Krasheninnikov, Arkady V

   ACS applied materials & interfaces 2020

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   Two-dimensional (2D) materials with nanometer-size holes are promising systems for DNA sequencing, water purification, and molecule selection/separation. However, controllable creation of holes with uniform sizes and shapes is still a challenge, especially when the 2D material consists of several atomic layers as, e.g., MoS2, the archetypical transition metal dichalcogenide. We use analytical potential molecular dynamics simulations to study the response of 2D MoS2 to cluster irradiation. We model both freestanding and supported sheets and assess the amount of damage created in MoS2 by the impacts of noble gas clusters in a wide range of cluster energies and incident angles. We show that cluster irradiation can be used to produce uniform holes in 2D MoS2 with the diameter being dependent on cluster size and energy. Energetic clusters can also be used to displace sulfur atoms preferentially from either top or bottom layers of S atoms in MoS2 and also clean the surface of MoS2 sheets from adsorbents. Our results for MoS2, which should be relevant to other 2D transition metal dichalcogenides, suggest new routes toward cluster beam engineering of devices based on 2D inorganic materials.

2. 

   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.