41 citations · 70 across the 4 of their papers we have counts for
4 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…
Disorder-dependent Li diffusion in investigated by machine learning potential
Jiho Lee, Suyeon Ju, Seungwoo Hwang +4
Solid-state electrolytes with argyrodite structures, such as , have attracted considerable attention due to their superior safety compared to liquid electrolyt…
Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials
Dongsun Yoo, Jisu Jung, Wonseok Jeong +1
The universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the trai…
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…