1 code implementation • 9 Dec 2023 • Hyuna Kwon, Tim Hsu, Wenyu Sun, Wonseok Jeong, Fikret Aydin, James Chapman, Xiao Chen, Matthew R. Carbone, Deyu Lu, Fei Zhou, Tuan Anh Pham
In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method to predict 3D structures of disordered materials from a target property.
1 code implementation • 17 Jul 2023 • Wei Chen, Yihui Ren, Ai Kagawa, Matthew R. Carbone, Samuel Yen-Chi Chen, Xiaohui Qu, Shinjae Yoo, Austin Clyde, Arvind Ramanathan, Rick L. Stevens, Hubertus J. J. van Dam, Deyu Lu
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
no code implementations • 11 Jan 2023 • Zhu Liang, Matthew R. Carbone, Wei Chen, Fanchen Meng, Eli Stavitski, Deyu Lu, Mark S. Hybertsen, Xiaohui Qu
A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray absorption near-edge structure (XANES) spectra.
1 code implementation • 16 Jul 2021 • Cole Miles, Matthew R. Carbone, Erica J. Sturm, Deyu Lu, Andreas Weichselbaum, Kipton Barros, Robert M. Konik
We employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions.