Matthew R. Carbone
Cited by
Cited by
Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships
SB Torrisi, MR Carbone, BA Rohr, JH Montoya, Y Ha, J Yano, SK Suram, ...
npj Computational Materials 6, 109, 2020
Classification of local chemical environments from x-ray absorption spectra using supervised machine learning
MR Carbone, S Yoo, M Topsakal, D Lu
Physical Review Materials 3 (3), 033604, 2019
Machine-learning X-ray absorption spectra to quantitative accuracy
MR Carbone, M Topsakal, D Lu, S Yoo
Physical Review Letters 124 (15), 156401, 2020
When not to use machine learning: A perspective on potential and limitations
MR Carbone
MRS Bulletin 47, 968–974, 2022
Bond-Peierls polaron: Moderate mass enhancement and current-carrying ground state
MR Carbone, AJ Millis, DR Reichman, J Sous
Physical Review B 104 (14), L140307, 2021
Microscopic model of the doping dependence of linewidths in monolayer transition metal dichalcogenides
MR Carbone, MZ Mayers, DR Reichman
The Journal of Chemical Physics 152 (19), 2020
Predicting impurity spectral functions using machine learning
EJ Sturm, MR Carbone, D Lu, A Weichselbaum, RM Konik
Physical Review B 103 (24), 245118, 2021
Numerically exact generalized Green's function cluster expansions for electron-phonon problems
MR Carbone, DR Reichman, J Sous
Physical Review B 104 (3), 035106, 2021
Uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles
A Ghose, M Segal, F Meng, Z Liang, MS Hybertsen, X Qu, E Stavitski, ...
Physical Review Research 5 (1), 013180, 2023
Machine learning of Kondo physics using variational autoencoders and symbolic regression
C Miles, MR Carbone, EJ Sturm, D Lu, A Weichselbaum, K Barros, ...
Physical Review B 104 (23), 235111, 2021
Effective Trap-like Activated Dynamics in a Continuous Landscape
MR Carbone, V Astuti, M Baity-Jesi
Physical Review E 101 (5), 052304, 2020
Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon
H Kwon, W Sun, T Hsu, W Jeong, F Aydin, S Sharma, F Meng, ...
The Journal of Physical Chemistry C 127 (33), 16473-16484, 2023
Competition between energy-and entropy-driven activation in glasses
MR Carbone, M Baity-Jesi
Physical Review E 106 (2), 024603, 2022
Self-driving multimodal studies at user facilities
PM Maffettone, DB Allan, SI Campbell, MR Carbone, TA Caswell, ...
arXiv preprint arXiv:2301.09177, 2023
Machine learning the spectral function of a hole in a quantum antiferromagnet
J Lee, MR Carbone, W Yin
Physical Review B 107 (20), 205132, 2023
Decoding structure-spectrum relationships with physically organized latent spaces
Z Liang, MR Carbone, W Chen, F Meng, E Stavitski, D Lu, MS Hybertsen, ...
Physical Review Materials 7 (5), 053802, 2023
Using machine learning to predict local chemical environments from x-ray absorption spectra
D Lu, M Carbone, M Topsakal, S Yoo
APS March Meeting Abstracts 2019, A18. 005, 2019
Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files
MR Carbone, F Meng, C Vorwerk, B Maurer, F Peschel, X Qu, E Stavitski, ...
Journal of Open Source Software 8 (5182), 2023
Flexible formulation of value for experiment interpretation and design
MR Carbone, HJ Kim, C Fernando, S Yoo, D Olds, H Joress, B DeCost, ...
Matter 7 (2), 685-696, 2024
Transferable graph neural fingerprint models for quick response to future bio-threats
W Chen, Y Ren, A Kagawa, MR Carbone, SYC Chen, X Qu, S Yoo, ...
2023 International Conference on Machine Learning and Applications (ICMLA …, 2023
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