41 citations · 81 across the 5 of their papers we have counts for
5 papers
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…
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…
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
Sungwoo Kang, Wonseok Jeong, Changho Hong +3
The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material spac…
Training machine-learning potentials for crystal structure prediction using disordered structures
Changho Hong, Jeong Min Choi, Wonseok Jeong +6
Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in explori…