activity
20192026
most citedAtomic energy mapping of neural network potential

41 citations · 112 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2026

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning

Wonseok Jeong, Francesca Tavazza, Brian DeCost

Atomic-scale modeling has advanced rapidly through integration of machine learning, yet a key bottleneck remains. Even with an accurate potential energy surface and a clear target…

cond-mat.mtrl-sci2023★ 1 cited

Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models

Hyuna Kwon, Tim Hsu, Wenyu Sun +8

The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework b…

cond-mat.mtrl-sci2022

E-beam-enhanced solid-state mechanical amorphization of alpha-quartz: Reducing deformation barrier via localized excess electrons as mobile anions

Sung-Gyu Kang, Wonseok Jeong, Hwangsun Kim +4

Under hydrostatic pressure, alpha-quartz undergoes solid-state mechanical amorphization wherein the interpenetration of SiO4 tetrahedra occurs and the material loses crystallinity.…

physics.comp-ph2020★ 29 cited

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…

physics.comp-ph2020★ 41 cited

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…

physics.comp-ph2019★ 41 cited

Atomic energy mapping of neural network potential

Dongsun Yoo, Kyuhyun Lee, Wonseok Jeong +2

We show that the intelligence of the machine-learning potential arises from its ability to infer the reference atomic-energy function from a given set of total energies. By utilizi…