This paper addresses the challenge of exploring potential energy surfaces to understand chemical reactions on surfaces, which is crucial for applications in catalysis, corrosion, and thin-film growth. Traditional methods such as the Activation-Relaxation Technique (ARTn) often struggle to identify transition states in complex surface reactions due to high computational demands and difficulties in navigating intricate energy landscapes. To overcome this, the study introduces iso-ARTn method, which utilizes constraints on an orthogonal hyperplane and an adaptive active volume to enhance search efficiency. When applied to a Pt(111) surface with 0.3 monolayers of oxygen coverage using a neural network potential, iso-ARTn achieved an 8.2% higher success rate and required 40% fewer force evaluations than the original ARTn. Moreover, combined with kinetic Monte Carlo simulation for Pt(111) oxidation, iso-ARTn revealed the structure consistent with experimental observations. This study paves the way for investigating complex surface processes by efficiently identifying elementary reactions through improved saddle point search. This work was published in Journal of Chemical Theory and Computation 2024, 20, 8024.

This research aims to advance the atomistic understanding of dry-etching processes crucial for semiconductor manufacturing. Current molecular dynamics (MD) simulations are limited by the lack of reliable force fields, hindering their application in etching studies. To address this, a neural network potential (NNP) was developed to simulate the etching of amorphous Si₃N₄ with HF molecules. The NNP was trained using surface reactions in diverse local environments: baseline structures, reaction-specific data, and general-purpose training sets; and refined through iterative comparisons with density functional theory (DFT) results to ensure high accuracy. The trained NNP allowed for detailed simulations of key aspects such as preferential sputtering, surface modification, etching yield, threshold energy, and product distribution. A continuum model, derived from MD results, was also developed to replicate surface composition changes effectively. This study establishes a robust computational framework for atomistic etching simulations, offering a pathway to more accurate and efficient etching process designs for the semiconductor industry. This work was published in ACS applied Materials & Interfaces 2024, 16, 48457.

Solid-state electrolytes with argyrodite structures, have attracted considerable attention due to their superior safety and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by disorder engineering, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio molecular dynamics (MD) simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in Li₆PS₅Cl at 300 K using large-scale, long-term MD simulations empowered by machine-learning potentials (MLPs). 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. In addition, Li-ion diffusion shows non-Arrhenius behavior, leading to different activation energies at high temperatures. These phenomena are explained by the interplay between inter- and intra-cage jumps. By elucidation of the key factors affecting Li-ion diffusion in Li₆PS₅Cl, this work paves the way for optimizing ionic conductivity in the argyrodite family. This study was published in ACS Applied Materials & Interfaces 2024, 16, 46442.

Improving Li-ion batteries to support higher voltages, faster charging, and wider temperature ranges requires better electrolyte materials, but exploring the vast combinations of solvents, salts, and additives is challenging using traditional experimental and computational methods. Here, we evaluated a state-of-the-art pretrained universal machine-learning interatomic potential (MLIP), SevenNet-0, for its ability to predict key properties of liquid electrolytes, including solvation structure, density, and ion transport. Although primarily trained on inorganic systems, SevenNet-0 performed well on organic electrolytes, showing good agreement with experimental and ab initio data, though we identified systematic errors, particularly in density predictions, and addressed them by fine-tuning the model. This approach improved accuracy significantly while keeping computational costs low, and analysis of the training set indicated that the model achieved its performance by generalizing across chemical space rather than memorizing specific configurations. This study highlights the potential of MLIPs like SevenNet-0 to accelerate the discovery and design of advanced electrolyte materials, and this work was published in Digital Discovery 2025, 4, 1544.