most citedData Distillation for Neural Network Potentials toward Foundational Dataset

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

physics.comp-ph2023★ 2 cited

Data Distillation for Neural Network Potentials toward Foundational Dataset

Gang Seob Jung, Sangkeun Lee, Jong Youl Choi

Machine learning (ML) techniques and atomistic modeling have rapidly transformed materials design and discovery. Specifically, generative models can swiftly propose promising mater…

cs.DC2023

Unraveling Diffusion in Fusion Plasma: A Case Study of In Situ Processing and Particle Sorting

Junmin Gu, Paul Lin, Kesheng Wu +6

This work starts an in situ processing capability to study a certain diffusion process in magnetic confinement fusion. This diffusion process involves plasma particles that are lik…

cs.LG2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Jong Youl Choi, Pei Zhang, Kshitij Mehta +2

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecu…