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Han, Seungwu

Publications and source records attributed to Han, Seungwu.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Electrochemical Degradation of Pt 3 Co Nanoparticles Investigated by Off-Lattice Kinetic Monte Carlo Simulations with Machine-Learned Potentials

In fuel cell applications, the durability of catalysts is critical for large-scale industrial implementation. However, limited synthesis controllability and spectroscopic resolution impede a comprehensive understanding of degradation mechanisms at the atomic level. In this study, we develop a machine-learned potential (MLP) to simulate the degradation processes for Pt3Co nanoparticles. The precision of MLP is determined to be comparable to that of density functional theory calculations. Using off-lattice kinetic Monte Carlo simulations with MLP, we successfully replicate established experimental trends and offer a logical resolution to ongoing debates regarding atomic orderings. Based on the simulation results, we suggest design principles for Pt3Co nanoparticles that combine high activity and durability. Finally, we validate the wide applicability of our method by successfully applying it to Pt3Ni and Pt3Co0.5Ni0.5 nanoparticles. The research serves as a guideline for developing MLPs for alloy electrochemical catalysts and lays the foundation for designing more durable and active fuel-cell catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

E-beam-enhanced solid-state mechanical amorphization of α-quartz: Reduced deformation barrier via localized excess electrons as network modifiers

Under hydrostatic pressure, α-quartz (α-SiO 2 ) undergoes solid-state mechanical amorphization wherein the interpenetration of [SiO 4 ] 2- tetrahedra occurs and the material loses crystallinity. This phase transformation requires a high hydrostatic pressure of 14GPa 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 α-quartz and enhances the solid-state mechanical amorphization at nanoscale. Specifically, during in situ uniaxial compression, a larger permanent deformation occurs in α-quartz submicron pillars 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 α-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. In conclusion, these findings deepen our understanding of electron–matter interactions and can be extended to new glass forming and processing technologies at nano- and microscale.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Athermal glass work at the nanoscale: Engineered electron-beam-induced viscoplasticity for mechanical shaping of brittle amorphous silica

Amorphous silica deforms viscoplastically at elevated temperatures, which is common for brittle glasses. The key mechanism of viscoplastic deformation involves interatomic bond switching, which is thermally activated. Here, for this study, we precisely control the mechanical shaping of brittle amorphous silica at the nanoscale via engineered electron–matter interactions without heating. We observe a ductile plastic deformation of amorphous silica under a focused scanning electron beam with low acceleration voltages (few to tens of kilovolts) during in-situ compression studies, with unique dependence on the acceleration voltage and beam current. By simulating the electron–matter interaction, we show that the deformation of amorphous silica depends strongly on the volume where inelastic scattering occurs. The electron–matter interaction via e-beam irradiation alters the Si–O interatomic bonds, enabling the high-temperature deformation behavior of amorphous silica to occur athermally. Finally, by systematically controlling the electron–matter interaction volume, it is possible to mechanically shape the brittle amorphous silica on a small scale at room temperature to a level comparable to glass shaping at high temperatures. The findings can be extended to develop new fabrication processes for nano- and microscale brittle glasses.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

We report with the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10 -1 and 10 3 Wm -1 K -1 . Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

36 MATERIALS SCIENCE↗