Matthew R. Carbone

According to our database, Matthew R. Carbone authored at least 9 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Universal Deep Learning Framework for Materials X-ray Absorption Spectra.

Shubha R. Kharel, Fanchen Meng, Xiaohui Qu, Matthew R. Carbone, Deyu Lu

CoRR, 2024

2023

The Generalized Green's function Cluster Expansion: A Python package for simulating polarons.

Matthew R. Carbone, Stepan Fomichev, Andrew J. Millis, Mona Berciu, David R. Reichman, John Sous

J. Open Source Softw., October, 2023

Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files.

Matthew R. Carbone, Fanchen Meng, Christian Vorwerk, Benedikt Maurer, Fabian Peschel, Xiaohui Qu, Eli Stavitski, Claudia Draxl, John Vinson, Deyu Lu

J. Open Source Softw., 2023

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

Hyuna Kwon, Tim Hsu, Wenyu Sun, Wonseok Jeong, Fikret Aydin, James Chapman, Xiao Chen, Matthew R. Carbone, Deyu Lu, Fei Zhou, Tuan Anh Pham

CoRR, 2023

Self-driving Multimodal Studies at User Facilities.

Phillip M. Maffettone, Daniel B. Allan, Stuart I. Campbell, Matthew R. Carbone, Thomas A. Caswell, Brian L. DeCost, Dmitri Gavrilov, Marcus D. Hanwell, Howie Joress, Joshua K. Lynch, Bruce Ravel, Stuart B. Wilkins, Jakub Wlodek, Daniel Olds

CoRR, 2023

Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces.

Zhu Liang, Matthew R. Carbone, Wei Chen, Fanchen Meng, Eli Stavitski, Deyu Lu, Mark S. Hybertsen, Xiaohui Qu

CoRR, 2023

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

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 Van Dam, Deyu Lu

Proceedings of the International Conference on Machine Learning and Applications, 2023

2022

When not to use machine learning: a perspective on potential and limitations.

Matthew R. Carbone

CoRR, 2022

2021

Machine-learning Kondo physics using variational autoencoders.

Cole Miles, Matthew R. Carbone, Erica J. Sturm, Deyu Lu, Andreas Weichselbaum, Kipton Barros, Robert M. Konik

CoRR, 2021