Local Structures of Ex-Solved Nanoparticles Identified by Machine-Learned Potentials

S Kang, JK Kim, H Kim, YH Son, J Chang, J Kim… - Nano Letters, 2024 - ACS Publications
In this study, we identify the local structures of ex-solved nanoparticles using machine-
learned potentials (MLPs). We develop a method for training machine-learned potentials by …

Fluoride-Ion Conduction by Synergic Rotation of the Anion Sublattice for Tl4.5SnF8.5 Analogues

T Takami, N Yasufuku, M Ivonina, T Tada… - Chemistry of …, 2024 - ACS Publications
Fluoride-ion conductors have attracted great attention as solid electrolytes for all-solid-state
fluoride-ion batteries with high energy densities surpassing those of conventional lithium-ion …

Disorder-Dependent Li Diffusion in Li6PS5Cl Investigated by Machine-Learning Potential

J Lee, S Ju, S Hwang, J You, J Jung… - ACS Applied Materials …, 2024 - ACS Publications
Solid-state electrolytes with argyrodite structures, such as Li6PS5Cl, have attracted
considerable attention due to their superior safety compared to liquid electrolytes and higher …

Atomistic Simulation of HF Etching Process of Amorphous Si3N4 Using Machine Learning Potential

C Hong, S Oh, H An, P Kim, Y Kim, J Ko… - … Applied Materials & …, 2024 - ACS Publications
An atomistic understanding of dry-etching processes with reactive molecules is crucial for
achieving geometric integrity in highly scaled semiconductor devices. Molecular dynamics …

Modified Activation-Relaxation Technique (ARTn) Method Tuned for Efficient Identification of Transition States in Surface Reactions

J Jung, H An, J Lee, S Han - Journal of Chemical Theory and …, 2024 - ACS Publications
Exploring potential energy surfaces (PES) is essential for unraveling the underlying
mechanisms of chemical reactions and material properties. While the activation-relaxation …

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

J Kim, J Kim, J Kim, J Lee, Y Park, Y Kang… - arXiv preprint arXiv …, 2024 - arxiv.org
Machine learning interatomic potentials (MLIPs) are used to estimate potential energy
surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with …