arXiv subjects

Seungwu Han

Publications and source records attributed to Seungwu Han.

33 records · Page 2Linked to original sources

arXiv2025

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Achieving higher operational voltages, faster charging, and broader temperature ranges for Li-ion batteries necessitates advancements in electrolyte engineering. However, the complexity of optimizing combinations of solvents, salts, and additives has limited the effectiveness of both experimental and computational screening methods for liquid electrolytes. Recently, pretrained universal machine-learning interatomic potentials (MLIPs) have emerged as promising tools for computational exploration of complex chemical spaces with high accuracy and efficiency. In this study, we evaluated the performance of the state-of-the-art equivariant pretrained MLIP, SevenNet-0, in predicting key properties of liquid electrolytes, including solvation behavior, density, and ion transport. To assess its suitability for extensive material screening, we considered a dataset comprising 20 solvents. Although SevenNet-0 was predominantly trained on inorganic compounds, its predictions for the properties of liquid electrolytes showed good agreement with experimental and $\textit{ab initio}$ data. However, systematic errors were identified, particularly in the predicted density of liquid electrolytes. To address this limitation, we fine-tuned SevenNet-0, achieving improved accuracy at a significantly reduced computational cost compared to developing bespoke models. Analysis of the training set suggested that the model achieved its accuracy by generalizing across the chemical space rather than memorizing specific configurations. This work highlights the potential of SevenNet-0 as a powerful tool for future engineering of liquid electrolyte systems.

cond-mat.mtrl-sci↗

arXiv2024

Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with reduced computational costs. However, the high cost of assembling high-fidelity databases hampers the application of MLIPs to systems that require high chemical accuracy. Utilizing an equivariant graph neural network, we present an MLIP framework that trains on multi-fidelity databases simultaneously. This approach enables the accurate learning of high-fidelity PES with minimal high-fidelity data. We test this framework on the Li$_6$PS$_5$Cl and In$_x$Ga$_{1-x}$N systems. The computational results indicate that geometric and compositional spaces not covered by the high-fidelity meta-gradient generalized approximation (meta-GGA) database can be effectively inferred from low-fidelity GGA data, thus enhancing accuracy and molecular dynamics stability. We also develop a general-purpose MLIP that utilizes both GGA and meta-GGA data from the Materials Project, significantly enhancing MLIP performance for high-accuracy tasks such as predicting energies above hull for crystals in general. Furthermore, we demonstrate that the present multi-fidelity learning is more effective than transfer learning or $Δ$-learning an d that it can also be applied to learn higher-fidelity up to the coupled-cluster level. We believe this methodology holds promise for creating highly accurate bespoke or universal MLIPs by effectively expanding the high-fidelity dataset.

cond-mat.mtrl-sci↗

arXiv2024

Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations

Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due to their data efficiency and high accuracy. However, parallelizing GNN-IPs poses challenges because multiple message-passing layers complicate data communication within the spatial decomposition method, which is preferred by many molecular dynamics (MD) packages. In this article, we propose an efficient parallelization scheme compatible with GNN-IPs and develop a package, SevenNet (Scalable EquiVariance-Enabled Neural NETwork), based on the NequIP architecture. For MD simulations, SevenNet interfaces with the LAMMPS package. Through benchmark tests on a 32-GPU cluster with examples of SiO$_2$, SevenNet achieves over 80% parallel efficiency in weak-scaling scenarios and exhibits nearly ideal strong-scaling performance as long as GPUs are fully utilized. However, the strong-scaling performance significantly declines with suboptimal GPU utilization, particularly affecting parallel efficiency in cases involving lightweight models or simulations with small numbers of atoms. We also pre-train SevenNet with a vast dataset from the Materials Project (dubbed `SevenNet-0') and assess its performance on generating amorphous Si$_3$N$_4$ containing more than 100,000 atoms. By developing scalable GNN-IPs, this work aims to bridge the gap between advanced machine learning models and large-scale MD simulations, offering researchers a powerful tool to explore complex material systems with high accuracy and efficiency.

cond-mat.mtrl-sci↗

arXiv2023

Disorder-dependent Li diffusion in $\mathrm{Li_6PS_5Cl}$ investigated by machine learning potential

Solid-state electrolytes with argyrodite structures, such as $\mathrm{Li_6PS_5Cl}$, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio MD simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$ at 300 K using large-scale, long-term MD simulations empowered by machine learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a $5\times5\times5$ supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites, rather than at 50% where the disorder is maximized. This phenomenon is explained by the interplay between inter-cage and intra-cage jumps. By elucidating the key factors affecting Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$, this work paves the way for optimizing ionic conductivity in the argyrodite family.

cond-mat.mtrl-sci↗

arXiv2023

Investigation of the mechanism of the anomalous Hall effects in Cr2Te3/(BiSb)2(TeSe)3 heterostructure

The interplay between ferromagnetism and the non-trivial topology has unveiled intriguing phases in the transport of charges and spins. For example, it is consistently observed the so-called topological Hall effect (THE) featuring a hump structure in the curve of the Hall resistance (Rxy) vs. a magnetic field (H) of a heterostructure consisting of a ferromagnet (FM) and a topological insulator (TI). The origin of the hump structure is still controversial between the topological Hall effect model and the multi-component anomalous Hall effect (AHE) model. In this work, we have investigated a heterostructure consisting of BixSb2-xTeySe3-y (BSTS) and Cr2Te3 (CT), which are well-known TI and two-dimensional FM, respectively. By using the so-called minor-loop measurement, we have found that the hump structure observed in the CT/BSTS is more likely to originate from two AHE channels. Moreover, by analyzing the scaling behavior of each amplitude of two AHE with the longitudinal resistivities of CT and BSTS, we have found that one AHE is attributed to the extrinsic contribution of CT while the other is due to the intrinsic contribution of BSTS. It implies that the proximity-induced ferromagnetic layer inside BSTS serves as a source of the intrinsic AHE, resulting in the hump structure explained by the two AHE model.

cond-mat.mtrl-sci↗

arXiv2022

E-beam-enhanced solid-state mechanical amorphization of alpha-quartz: Reducing deformation barrier via localized excess electrons as mobile anions

Under hydrostatic pressure, alpha-quartz undergoes solid-state mechanical amorphization wherein the interpenetration of SiO4 tetrahedra occurs and the material loses crystallinity. This phase transformation requires a high hydrostatic pressure of 14 GPa because the repulsive forces resulting from the ionic nature of the Si-O bonds prevent the severe distortion of the atomic configuration. Herein, we experimentally and computationally demonstrate that e-beam irradiation changes the nature of the interatomic bonds in alpha-quartz and enhances the solid-state mechanical amorphization at nanoscale. Specifically, during in situ uniaxial compression, a larger permanent deformation occurs in alpha-quartz micropillars compressed during e-beam irradiation than in those without e-beam irradiation. Microstructural analysis reveals that the large permanent deformation under e-beam irradiation originates from the enhanced mechanical amorphization of alpha-quartz and the subsequent viscoplastic deformation of the amorphized region. Further, atomic-scale simulations suggest that the delocalized excess electrons introduced by e-beam irradiation move to highly distorted atomic configurations and alleviate the repulsive force, thus reducing the barrier to the solid-state mechanical amorphization. These findings deepen our understanding of electron-matter interactions and can be extended to new glass forming and processing technologies at nano- and microscale.

cond-mat.mtrl-sci↗

arXiv2022

$\textit{Ab initio}$ construction of full phase diagram of MgO-CaO eutectic system using neural network interatomic potentials

While several studies confirmed that machine-learned potentials (MLPs) can provide accurate free energies for determining phase stabilities, the abilities of MLPs for efficiently constructing a full phase diagram of multi-component systems are yet to be established. In this work, by employing neural network interatomic potentials (NNPs), we demonstrate construction of the MgO-CaO eutectic phase diagram with temperatures up to 3400 K, which includes liquid phases. The NNP is trained over trajectories of various solid and liquid phases at several compositions that are calculated within the density functional theory (DFT). For the exchange-correlation energy among electrons, we compare the PBE and SCAN functionals. The phase boundaries such as solidus, solvus, and liquidus are determined by free-energy calculations based on the thermodynamic integration or semigrand ensemble methods, and salient features in the phase diagram such as solubility limit and eutectic points are well reproduced. In particular, the phase diagram produced by the SCAN-NNP closely follows the experimental data, exhibiting both eutectic composition and temperature within the measurements. On a rough estimate, the whole procedure is more than 1,000 times faster than pure-DFT based approaches. We believe that this work paves the way to fully $\textit{ab initio}$ calculation of phase diagrams.

physics.comp-ph↗

arXiv2021

Understanding the roles of electronic effect in CO on Pt-Sn alloy surface via band structure measurements

Using angle-resolved photoemission spectroscopy, we show the direct evidence of charge transfer between adsorbed molecules and metal substrate, i.e. chemisorption of CO on Pt(111) and Pt-Sn/Pt(111) 2x2 surfaces. The observed band structure shows a unique signature of charge transfer as CO atoms are adsorbed,revealing the roles of specific orbital characters participating in the chemisorption process. As the coverage of CO increases, the degree of charge transfer between CO and Pt shows clear difference to that of Pt-Sn. With comparison to DFT calculation results, the observed distinct features in the band structure are interpreted as backdonation bonding states of Pt molecular orbital to the 2π orbital of CO. Furthermore, the change in the surface charge concentration, measured from the Fermi surface area, shows Pt surface has a larger charge concentration change than Pt-Sn surface upon CO adsorption. The difference in the charge concentration change between Pt and Pt-Sn surfaces reflects the degree of electronic effects during CO adsorption on Pt-Sn.

cond-mat.mtrl-sci↗

arXiv2021

Tailoring topological Hall effect in SrRuO3/SrTiO3 superlattices

Investigating the effects of the complex magnetic interactions on the formation of nontrivial magnetic phases enables a better understanding of magnetic materials. Moreover, an effective method to systematically control those interactions and phases could be extensively utilized in spintronic devices. SrRuO3 heterostructures function as a suitable material system to investigate the complex magnetic interactions and the resultant formation of topological magnetic phases, as the heterostructuring approach provides an accessible controllability to modulate the magnetic interactions. In this study, we have observed that the Hall effect of SrRuO3/SrTiO3 superlattices varies nonmonotonically with the repetition number (z). Using Monte Carlo simulations, we identify a possible origin of this experimental observation: the interplay between the Dzyaloshinskii-Moriya interaction and dipole-dipole interaction, which have differing z-dependence, might result in a z-dependent modulation of topological magnetic phases. This approach provides not only a collective understanding of the magnetic interactions in artificial heterostructures but also a facile control over the skyrmion phases.

cond-mat.str-el↗

arXiv2021

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the computational crystal structure prediction is expected to mitigate this frustration, the NP-hardness and steep costs of density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a highly efficient and reliable structure-prediction framework based on exhaustive random searches and evolutionary algorithms, which is completely free from empiricism. Empowered by accurate neural network potentials, the program can navigate the configuration space faster than DFT by more than 10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected from the experimental database, SPINNER successfully identifies experimental (or theoretically more stable) phases for ~80% of materials within 5000 generations, entailing up to half a million structure evaluations for each composition. When benchmarked against previous data mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in the majority of cases. By developing a reliable and fast structure-prediction framework, this work opens the door to large-scale, unbounded computational exploration of undiscovered inorganic crystals.

physics.comp-ph↗

arXiv2020

Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials

The universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the training set should encompass diverse local atomic environments but the conventional approach is prone to sampling similar configurations repeatedly, mainly due to the Boltzmann statistics. As such, practitioners handpick a large pool of distinct configurations manually, stretching the development period significantly. Herein, we suggest a novel sampling method optimized for gathering diverse yet relevant configurations semi-automatically. This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable. As a result, the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy. We apply the proposed metadynamics sampling to H:Pt(111), GeTe, and Si systems. Throughout the examples, a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs. By proposing a semi-automatic sampling method tuned for MLPs, the present work paves the way to wider applications of MLPs to many challenging applications.

physics.comp-ph↗

arXiv2020

Training machine-learning potentials for crystal structure prediction using disordered structures

Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in exploring the vast configurational space. The machine-learning potential such as the neural network potential (NNP) is poised to meet this requirement but a dearth of information on the crystal structure poses a challenge in choosing training sets. Herein we propose constructing the training set from densityfunctional-theory (DFT) based dynamical trajectories of liquid and quenched amorphous phases, which does not require any preceding information on material structures except for the chemical composition. To demonstrate suitability of the trained NNP in the crystal structure prediction, we compare NNP and DFT energies for Ba2AgSi3, Mg2SiO4, LiAlCl4, and InTe2O5F over experimental phases as well as low-energy crystal structures that are generated theoretically. For every material, we find strong correlations between DFT and NNP energies, ensuring that the NNPs can properly rank energies among low-energy crystalline structures. We also find that the evolutionary search using the NNPs can identify low-energy metastable phases more efficiently than the DFTbased approach. By proposing a way to developing reliable machine-learning potentials for the crystal structure prediction, this work will pave the way to identifying unexplored multinary phases efficiently.

physics.comp-ph↗

arXiv2019

Atomic energy mapping of neural network potential

We show that the intelligence of the machine-learning potential arises from its ability to infer the reference atomic-energy function from a given set of total energies. By utilizing invariant points in the feature space at which the atomic energy has a fixed reference value, we examine the atomic energy mapping of neural network potentials. Through a series of examples on Si, we demonstrate that the neural network potential is vulnerable to 'ad hoc' mapping in which the total energy appears to be trained accurately while the atomic energy mapping is incorrect in spite of its capability. We show that the energy mapping can be improved by choosing the training set carefully and monitoring the atomic energy at the invariant points during the training procedure.

physics.comp-ph↗

arXiv2005

Reduction of Activation Energy Barrier of Stone-Wales Transformation in Endohedral Metallofullerenes

We examine effects of encapsulated metal atoms inside a C$_{60}$ molecule on the activation energy barrier to the Stone-Wales transformation using {\it ab initio} calculations. The encapsulated metal atoms we study are K, Ca and La which nominally donate one, two and three electrons to the C$_{60}$ cage, respectively. We find that isomerization of the endohedral metallofullerene via the Stone-Wales transformation can occur more easily than that of the empty fullerene owing to the charge transfer. When K, Ca and La atoms are encapsulated inside the fullerene, the activation energy barriers are lowered by 0.30, 0.55 and 0.80 eV, respectively compared with that of the empty C$_{60}$ (7.16 eV). The lower activation energy barrier of the Stone-Wales transformation implies the higher probability of isomerization and coalescence of metallofullerenes, which require a series of Stone-Wales transformations.

cond-mat.mes-hall↗

arXiv2004

Strongly Non-Arrhenius Self-Interstitial Diffusion in Vanadium

We study diffusion of self-interstitial atoms (SIAs) in vanadium via molecular dynamics simulations. The <111>-split interstitials are observed to diffuse one-dimensionally at low temperature, but rotate into other <111> directions as the temperature is increased. The SIA diffusion is highly non-Arrhenius. At T<600 K, this behavior arises from temperature-dependent correlations. At T>600 K, the Arrhenius expression for thermally activated diffusion breaks down when the migration barriers become small compared to the thermal energy. This leads to Arrhenius diffusion kinetics at low T and diffusivity proportional to temperature at high T.

cond-mat.mtrl-sci↗