Matthew R. Carbone

Brookhaven National Laboratory — United States

Materials Science · Physical Sciences

15h-index846citations90works5.52yr avg
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Research Topics

Machine Learning in Materials Science(37), X-ray Diffraction in Crystallography(23), Computational Drug Discovery Methods(17), Physics of Superconductivity and Magnetism(8), Theoretical and Computational Physics(6)

Publications90 total

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation
Photon Science·2026Open Access
Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models
Photon Science·2025· 1 cited
Autonomous Nanoparticle Synthesis Guided by <i>In Situ</i> Multiscale Structural Characterization
Journal of the American Chemical Society·2025· 1 cited
Harnessing Machine Learning to Advance Amorphous Battery Materials
ECS Meeting Abstracts·2025
Strain release through hydrogen bond–mediated layer twisting
Science Advances·2025Open Access
Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models
arXiv (Cornell University)·2025Open Access
Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning
Physical review. B./Physical review. B·2025Open Access
Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE
Journal of Applied Crystallography·2025· 3 cited
OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra
Physical Review Materials·2025· 4 citedOpen Access
Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential
PRX Energy·2025· 7 citedOpen Access
Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System
IEEE Access·2025· 8 citedOpen Access
Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node
arXiv (Cornell University)·2024Open Access
CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning
arXiv (Cornell University)·2024Open Access
Elucidating the Discharge Behavior of Aqueous Zinc Sulfur Batteries in the Presence of Molybdenum(IV) Chalcogenide Catalyst: The Criticality of Interfacial Electrochemistry
ACS Applied Materials & Interfaces·2024· 3 citedOpen Access
Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models
Machine Learning Science and Technology·2024· 11 citedOpen Access
OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
arXiv (Cornell University)·2024Open Access
In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination
Small Methods·2024· 1 citedOpen Access
Machine learning for the advancement of membrane science and technology: A critical review
Journal of Membrane Science·2024· 51 citedOpen Access
Machine learning-guided discovery of polymer membranes for CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si41.svg" display="inline" id="d1e198"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> separation with genetic algorithm
Journal of Membrane Science·2024· 21 citedOpen Access
Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning
Microscopy and Microanalysis·2024· 3 citedOpen Access
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Frequent Co-authors

Deyu Lu(21), Shinjae Yoo(11), Xiaohui Qu(6), Tejus Shastry(5), Fanchen Meng(5), Chuntian Cao(5), Phillip M. Maffettone(5), David R. Reichman(4), John Sous(4), Yasemin Basdogan(4), Sanat K. Kumar(4), Zhen‐Gang Wang(4), Daniel Olds(4), Howie Joress(4), Brian DeCost(4), Bruce Ravel(4), Yugang Zhang(4), Steven B. Torrisi(3), Junko Yano(3), Santosh K. Suram(3)