arXiv subjects

Sangmin Oh

Publications and source records attributed to Sangmin Oh.

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

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials

Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient. However, this approach often induces catastrophic forgetting, undermining the generalizability that is a key advantage of pretrained MLIPs. Herein, we propose reEWC, an advanced fine-tuning strategy that integrates Experience Replay and Elastic Weight Consolidation (EWC) to effectively balance forgetting prevention with fine-tuning efficiency. Using Li$_6$PS$_5$Cl (LPSC), a sulfide-based Li solid-state electrolyte, as a fine-tuning target, we show that reEWC significantly improves the accuracy of a pretrained MLIP, resolving well-known issues of potential energy surface softening and overestimated Li diffusivities. Moreover, reEWC preserves the generalizability of the pretrained MLIP and enables knowledge transfer to chemically distinct systems, including other sulfide, oxide, nitride, and halide electrolytes. Compared to Experience Replay and EWC used individually, reEWC delivers clear synergistic benefits, mitigating their respective limitations while maintaining computational efficiency. These results establish reEWC as a robust and effective solution for continual learning in MLIPs, enabling universal models that can advance materials research through large-scale, high-throughput simulations across diverse chemistries.

cond-mat.mtrl-sci↗