Matthew Carbone

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-4827
mcarbone@bnl.gov

Pronouns: he, him, his

Expertise | Research | Education | Appointments | Publications | Awards | Certifications

Expertise

• Theoretical condensed matter & spectroscopy
• Machine learning & data analysis
• High-performance computing
• Software development (primarily Python & C++)

Research Activities

Matt works closely with researchers at BNL's light source and nanocenter, as well as other DOE national labs, and the Air Force Research Lab. His primary research interests are, in no particular order (with some highlighted works hyperlinked):

• Machine learning/informatics for chemistry, materials and physics
• Development and deployment of novel autonomous experimentation algorithms, particularly for epistemic research
• Scientific software development (see e.g. Lightshow, GPax, GGCE and hdspin)
• Fundamental condensed matter research for Green functions methods development and spin glasses

You can also find me on Google Scholar or my personal website. Select high-impact publications can be found below.

Education

• Ph. D. in chemical physics; Columbia University, New York, NY (2021)
• M. A. in chemical physics; Columbia University, New York, NY (2017)
• B. S. magna cum laude in chemistry, with highest distinction; University of Rochester, Rochester, NY (2016)
• B. A. magna cum laude in physics, with highest distinction; University of Rochester, Rochester, NY (2016)

Professional Appointments

• Assistant Computational Scientist (RS3); Brookhaven National Laboratory (2021-present)

Selected Publications

• Carbone MR, Kim HJ, Fernando C, et al (2024) Flexible formulation of value for experiment interpretation and design. Matter 7:685–696. https://doi.org/10.1016/j.matt.2023.11.012
• Carbone MR (2022) When not to use machine learning: A perspective on potential and limitations. MRS Bulletin 47:968–974. https://doi.org/10.1557/s43577-022-00417-z
• Torrisi SB, Carbone MR, Rohr BA, et al (2020) Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships. npj Computational Materials 6:. https://doi.org/10.1038/s41524-020-00376-6
• Carbone MR, Topsakal M, Lu D, Yoo S (2020) Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy. Physical Review Letters 124:. https://doi.org/10.1103/physrevlett.124.156401

Awards & Recognition

• George Pegram Award for Meritorious Achievement in Chemical Research (2021)
• Department of Energy Computational Science Graduate Fellowship (2017)
• Dr. E.W. and Maude V. Flagg Award (2016)
• Junior Scholar Award (2015)
• Carl A. Whiteman Jr. Teaching Award (2015)

Certifications

• Teaching Development Program Advanced Certification; Columbia University, New York, NY (2021)