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Publications

For an automatically updating list of citable work, please of course see my Google Scholar (as I only periodically update my site). I do host PDF's here when possible, though! You can find a copy of my dissertation here. * Indicates equally contributing authors.

Preprints

  1. C. Cao*, M. R. Carbone*, C. Komurcuoglu, J. S. Shekhawat, K. Sun, H. Guo, S. Liu, K. Chen, S.-M. Bak, Y. Du, C. Weiland, X. Tong, D. Steingart, S. Yoo, N. Artrith, A. Urban, D. Lu & F. Wang. Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes. arXiv:2310.00794 (2023). [Accepted: Cell Reports Physical Science]
  2. Y. Basdogan, D. R. Pollard, T. Shastry, M. R. Carbone, S. K. Kumar & Z.-G. Wang. Machine Learning-Guided Discovery of Polymer Membranes for CO2 Separation. chemrxiv-2023-5h4s7 (2023).
  3. H. Kwon, T. Hsu, W. Sun, W. Jeong, F. Aydin, J. Chapman, X. Chen, M. R. Carbone, D. Lu, F. Zhou & T. A. Pham. Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models. arXiv:2312.05472 (2023). [Under review: npj Computational Materials]

2024

  1. M. R. Carbone, P. M. Maffettone, X. Qu, S. Yoo & D. Lu. Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy. The Journal of Physical Chemistry A 128, 1948 (2024).
  2. T. Shastry, Y. Basdogan, Z.-G. Wang, S. K. Kumar & M. R. Carbone. Machine learning-based discovery of molecular descriptors that control polymer gas permeation. The Journal of Membrane Science 697, 122563 (2024).
  3. S. Goswami, K. Barros & M. R. Carbone. Physically interpretable approximations of many-body spectral functions. Physical Review E 109, 015302 (2024). [PDF]
  4. M. R. Carbone, H. J. Kim, C. Fernando, S. Yoo, D. Olds, H. Joress, B. DeCost, B. Ravel, Y. Zhang & P. M. Maffettone. Flexible formulation of value for experiment interpretation and design. Matter 7, 685 (2024). [PDF]

2023

  1. M. R. Carbone*, S. Fomichev*, A. J. Millis, M. Berciu, D. R. Reichman & J. Sous. The Generalized Green's function Cluster Expansion: A Python package for simulating polarons. The Journal of Open Source Software 8, 5115 (2023). [GitHub]
  2. W. Chen, Y. Ren, A. Kagawa, M. R. Carbone, S. Y.-C. Chen, X. Qu, S. Yoo, A. Clyde, A. Ramanathan, R. L. Stevens, H. J. J. van Dam & D. Lu. Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats. arXiv.2308.01921 (2023). [Accepted: ICLMA 2023]
  3. H. Kwon, W. Sun, T. Hsu, W. Jeong, F. Aydin, S. Sharma, F. Meng, M. R. Carbone, X. Chen, D. Lu, L. F. Wan, M. H. Nielsen & T. A. Pham. Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon. The Journal of Physical Chemistry C 127, 16473 (2023).
  4. M. R. Carbone*, F. Meng*, C. Vorwerk, B. Maurer, F. Peschel, X. Qu, E. Stavitski, C. Draxl, J. Vinson & D. Lu. Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files. The Journal of Open Source Software 8, 5182 (2023). [GitHub]
  5. H. Guo*, M. R. Carbone*, C. Cao, J. Qu, Y. Du, S. Bak, C. Weiland, F. Wang, S. Yoo, N. Artrith, A. Urban & D. Lu. Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes. Scientific Data 10, 349 (2023).
  6. J. Lee, M. R. Carbone & W. Yin. Machine-learning the spectral function of a hole in a quantum antiferromagnet. Physical Review B 107, 205132 (2023). [PDF]
  7. Z. Liang, M. R. Carbone, W. Chen, F. Meng, E. Stavitski, D. Lu, M. S. Hybertsen & X. Qu. Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces. Physical Review Materials 7, 053802 (2023). [PDF]
  8. A. Ghose, M. Segal, F. Meng, Z. Liang, M. S. Hybertsen, X. Qu, E. Stavitski, S. Yoo, D. Lu & M. R. Carbone. Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles. Physical Review Research 5, 013180 (2023).

2022

  1. P. M. Maffettone, D. B. Allan, S. I. Campbell, M. R. Carbone, T. A. Caswell, B. L. DeCost, D. Gavrilov, M. D. Hanwell, H. Joress, J. Lynch, B. Ravel, S. B. Wilkins, J. Wlodek & D. Olds. Self-driving Multimodal Studies at User Facilities. arXiv:2301.09177 (2023). [Presented at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)]
  2. M. R. Carbone. When not to use machine learning: A perspective on potential and limitations. MRS Bulletin 47, 968–974 (2022).
  3. M. R. Carbone & M. Baity-Jesi. Competition between Barrier- and Entropy-Driven Activation in Glasses. Physical Review E 106, 024603 (2022). [PDF]
  4. S. B. Torrisi, J. M. Gregoire, J. Yano, M. R. Carbone, C. P. Gomes, L. Hung & S. K. Suram. Accelerated Materials Discovery: How to Use Artificial Intelligence to Speed Up Development / Chapter 3: Artificial intelligence for materials spectroscopy. Berlin, Boston: De Gruyter (2022).

2021

  1. C. Miles, M. R. Carbone, E. J. Sturm, D. Lu, A. Weichselbaum, K. Barros & R. M. Konik. Machine learning of Kondo physics using variational autoencoders and symbolic regression. Physical Review B 104, 235111 (2021). [PDF]
  2. M. R. Carbone, A. J. Millis, D. R. Reichman & J. Sous. Bond Peierls polaron: Moderate mass enhancement and current carrying ground state. Physical Review B 104, L140307 (2021). [PDF]
  3. M. R. Carbone, D. R. Reichman & J. Sous. Numerically Exact Generalized Green’s Function Cluster Expansions for Electron-Phonon Problems. Physical Review B 104, 035106 (2021). [PDF]
  4. E. J. Sturm*, M. R. Carbone*, D. Lu, A. Weichselbaum & R. M. Konik. Computing Anderson Impurity Model Spectra Using Machine Learning. Physical Review B 103, 245118 (2021). [PDF]

2020

  1. S. B. Torrisi, M. R. Carbone, B. A. Rohr, J. H. Montoya, Y. Ha, J. Yano, S. K. Suram & L. Hung. Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships. npj Computational Materials 6, 109 (2020).
  2. M. R. Carbone, M. Z. Mayers & D. R. Reichman. Microscopic model of the doping dependence of linewidths in monolayer transition metal dichalcogenides. The Journal of Chemical Physics 152, 194705 (2020). [PDF]
  3. M. R. Carbone, V. Astuti & M. Baity-Jesi. Effective Trap-like Activated Dynamics in a Continuous Landscape. Physical Review E 101, 052304 (2020). [PDF]
  4. M. R. Carbone, M. Topsakal, D. Lu & S. Yoo. Machine-learning X-ray absorption spectra to quantitative accuracy. Physical Review Letters 124, 156401 (2020). [PDF]

<2020

  1. M. R. Carbone, S. Yoo, M. Topsakal & D. Lu. Classification of local chemical environments from x-ray absorption spectra using supervised machine learning. Physical Review Materials 3, 033604 (2019). [PDF]
  2. M. R. Carbone, G. A. Centola, A. Haas, K. P. McClelland, M. D. Moskowitz, A. M. Verderame, M. S. Olezeski, L. J. Papa, S. C. M. Dorn, W. W. Brennessel & D. J. Weix. Crystal structures of (RS)-N-[(1R,2S)-2-benzyloxy-1-(2,6-dimethylphenyl)propyl]-2-methylpropane-2-sulfinamide and (RS)-N-[(1S,2R)-2-benzyloxy-1-(2,4,6-trimethylphenyl)propyl]-2-methylpropane-2-sulfinamide: two related protected 1,2-amino alcohols. Acta Crystallographica E70, 365-369 (2014).