Artikel dengan mandat akses publik - Matthew R. CarbonePelajari lebih lanjut
Tersedia di suatu tempat: 25
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
Mandat: US Department of Energy
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
Mandat: US Department of Energy
Machine-learning X-ray absorption spectra to quantitative accuracy
MR Carbone, M Topsakal, D Lu, S Yoo
Physical Review Letters 124 (15), 156401, 2020
Mandat: US Department of Energy
When not to use machine learning: A perspective on potential and limitations
MR Carbone
MRS Bulletin 47, 968–974, 2022
Mandat: US Department of Energy
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
Mandat: US National Science Foundation, US Department of Energy
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
Mandat: US National Science Foundation, US Department of Energy
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
Mandat: US Department of Energy
Predicting impurity spectral functions using machine learning
EJ Sturm, MR Carbone, D Lu, A Weichselbaum, RM Konik
Physical Review B 103 (24), 245118, 2021
Mandat: US Department of Energy
Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes
H Guo, MR Carbone, C Cao, J Qu, Y Du, SM Bak, C Weiland, F Wang, ...
Scientific data 10 (1), 349, 2023
Mandat: US Department of Energy, US National Institutes of Health
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
Mandat: US National Science Foundation, US Department of Energy, US National …
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
Mandat: US Department of Energy
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
Mandat: US Department of Energy
Effective Trap-like Activated Dynamics in a Continuous Landscape
MR Carbone, V Astuti, M Baity-Jesi
Physical Review E 101 (5), 052304, 2020
Mandat: US Department of Energy, Government of Spain
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
Mandat: US Department of Energy
Competition between energy-and entropy-driven activation in glasses
MR Carbone, M Baity-Jesi
Physical Review E 106 (2), 024603, 2022
Mandat: US Department of Energy
Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy
MR Carbone, PM Maffettone, X Qu, S Yoo, D Lu
The Journal of Physical Chemistry A 128 (10), 1948-1957, 2024
Mandat: US Department of Energy
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
Mandat: US Department of Energy
Machine learning-based discovery of molecular descriptors that control polymer gas permeation
T Shastry, Y Basdogan, ZG Wang, SK Kumar, MR Carbone
Journal of Membrane Science 697, 122563, 2024
Mandat: US Department of Energy
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
Mandat: US Department of Energy
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
Mandat: US Department of Energy
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