5 papers
A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
Sangmin Oh, Jinmu You, Jaesun Kim +4
We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowled…
Are diffusion models ready for materials discovery in unexplored chemical space?
Sanghyun Kim, Gihyeon Jeon, Seungwoo Hwang +4
While diffusion models are attracting increasing attention for the design of novel materials, their ability to generate low-energy structures in unexplored chemical spaces has not…
Discovery of oxide Li-conducting electrolytes in uncharted chemical space via topology-constrained crystal structure prediction
Seungwoo Hwang, Jiho Lee, Seungwu Han +2
Oxide Li-conducting solid-state electrolytes (SSEs) offer excellent chemical and thermal stability but typically exhibit lower ionic conductivity than sulfides and chlorides. This…
Atomistic Insights into Cu/amorphous-TaN Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction
Jeong Min Choi, Jaehoon Kim, Ji-Hwan Lee +2
Accurate understanding and control of interfacial adhesion between Cu and TaN diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu inter…
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh +5
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient altern…