16 citations · 16 across the 2 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2021
Scale-invariant Machine-learning Model Accelerates the Discovery of Quaternary Chalcogenides with Ultralow Lattice Thermal Conductivity
Koushik Pal, Cheol Woo Park, Yi Xia +2
Intrinsically low lattice thermal conductivity () is a desired requirement in many crystalline solids such as thermal barrier coatings and thermoelectrics. Here, we design an…
physics.comp-ph2020★ 16 cited
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause +3
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural n…
physics.comp-ph2019
Developing an improved Crystal Graph Convolutional Neural Network framework for accelerated materials discovery
Cheol Woo Park, Chris Wolverton
The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly versatile and accurate machine learning (ML) framework by learning material properties dire…