Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models

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.

Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats

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.

Drug Discovery Molecular Docking

Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces

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.

Machine learning of Kondo physics using variational autoencoders and symbolic regression

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.

BIG-bench Machine Learning regression +1