| 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 | 125 | 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 | 105 | 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 | 101 | 2020 |
| When not to use machine learning: A perspective on potential and limitations MR Carbone MRS Bulletin 47, 968–974, 2022 | 41 | 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 | 25 | 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 | 20 | 2020 |
| 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 | 19 | 2023 |
| Predicting impurity spectral functions using machine learning EJ Sturm, MR Carbone, D Lu, A Weichselbaum, RM Konik Physical Review B 103 (24), 245118, 2021 | 19 | 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 | 15 | 2021 |
| 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 | 14 | 2021 |
| 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 | 11 | 2023 |
| 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 | 11 | 2023 |
| Effective Trap-like Activated Dynamics in a Continuous Landscape MR Carbone, V Astuti, M Baity-Jesi Physical Review E 101 (5), 052304, 2020 | 9 | 2020 |
| 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 | 8 | 2023 |
| Self-driving multimodal studies at user facilities PM Maffettone, DB Allan, SI Campbell, MR Carbone, TA Caswell, ... arXiv preprint arXiv:2301.09177, 2023 | 8 | 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 | 6 | 2023 |
| Competition between energy-and entropy-driven activation in glasses MR Carbone, M Baity-Jesi Physical Review E 106 (2), 024603, 2022 | 6 | 2022 |
| 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 | 4* | 2024 |
| 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 | 4 | 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 | 4 | 2019 |
Deyu LuBrookhaven National LaboratoryVerified email at bnl.gov
