arXiv subjects

Hyungmin An

Publications and source records attributed to Hyungmin An.

3 recordsLinked to original sources

arXiv2026

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations

We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowledge-distillation framework. The model inherits the broad generalization capability of a large multi-task foundation model, SevenNet-Omni, trained on diverse materials datasets across chemical, configurational, and computational spaces. By learning chemical representations from high-quality inference data generated by the teacher model within a unified computational framework, SevenNet-Nano achieves high accuracy and strong transferability despite its compact architecture. The model also accurately captures a wide range of interatomic interactions, enabling reliable simulations under both equilibrium and extreme conditions, including plasma etching of SiO$_2$. Comprehensive benchmarks on static and dynamical properties--such as Li-ion diffusion and liquid densities--demonstrate its broad applicability with minimal fine-tuning. Importantly, SevenNet-Nano significantly reduces computational cost, achieving over an order-of-magnitude speedup and enabling large-scale atomistic simulations involving thousands of atoms.

cond-mat.mtrl-sci↗

arXiv2025

Etching-to-deposition transition in SiO$_2$/Si$_3$N$_4$ using CH$_x$F$_y$ ion-based plasma etching: An atomistic study with neural network potentials

Plasma etching, a critical process in semiconductor fabrication, utilizes hydrofluorocarbons both as etchants and as precursors for carbon film formation, where precise control over film growth is essential for achieving high SiO$_2$/Si$_3$N$_4$ selectivity and enabling atomic layer etching. In this work, we develop neural network potentials (NNPs) to gain atomistic insights into the surface evolution of SiO$_2$ and Si$_3$N$_4$ under hydrofluorocarbon ion bombardment. To efficiently sample diverse local configurations without exhaustive enumeration of ion-substrate combinations, we propose a vapor-to-surface sampling approach using high-temperature, low-density molecular dynamics simulations, supplemented with baseline reference structures. The NNPs, refined through iterative training, yield etching characteristics in MD simulations that show good agreement with experimental results. Further analysis reveals distinct mechanisms of carbon layer formation in SiO$_2$ and Si$_3$N$_4$, driven by the higher volatility of carbon-oxygen byproducts in SiO$_2$ and the suppressed formation of volatile carbon-nitrogen species in Si$_3$N$_4$. This computational framework enables quantitative predictions of atomistic surface modifications under plasma exposure and provides a foundation for integration with multiscale process modeling, offering insights into semiconductor fabrication processes.

cond-mat.mtrl-sci↗

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↗