1.2k citations · 1.5k across the 5 of their papers we have counts for
5 papers
Score-based denoising for atomic structure identification
Tim Hsu, Babak Sadigh, Nicolas Bertin +4
We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iterat…
Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
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…
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…
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…