Brenda M. Rubenstein Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

John Brown University

faculty

18 h-index 136 pubs 1,260 cited

Research Areas

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OverviewAI-generated summary

Dr. Brenda Rubenstein's research spans diverse areas of chemical physics and materials science at John Brown University. She applies machine learning techniques to predict protein conformations, as demonstrated by her work involving subsampled AlphaFold2. Rubenstein also investigates the structural, magnetic, and phonon properties of materials, including monolayer CrI3, using first-principles methods. Her theoretical interests extend to the foundations of computation, exploring the connection between stochastic thermodynamics and the energy costs of computation. Additionally, she employs topological data analysis to uncover biophysical signatures in protein dynamics and develops machine learning models, such as LYRUS, to predict the pathogenicity of missense variants. Her broader research interests include the physics of superconductivity and magnetism, cold atom physics and Bose-Einstein condensates, and quantum superfluid helium dynamics.

Metrics

h-index: 18
Publications: 136
Citations: 1,260

Selected Publications

Atomistic descriptor optimization using complementary Euclidean and geodesic distance information (2024) DOI
Disentangling the physics of the attractive Hubbard model as a fully interacting model of fermions via the accessible and symmetry-resolved entanglement entropies (2024) DOI
Self‐Consistent Convolutional Density Functional Approximations: Application to Adsorption at Metal Surfaces (2024) DOI
Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold2 (2023) DOI
Leveraging autocatalytic reactions for chemical domain image classification (2021) DOI