22 citations · 28 across the 4 of their papers we have counts for
4 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…
Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm
James Chapman, Tim Hsu, Xiao Chen +2
Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we lever…
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy
Tim Hsu, Tuan Anh Pham, Nathan Keilbart +6
Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does n…
Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
Tim Hsu, William K. Epting, Hokon Kim +5
Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of…