Brenda M. Rubenstein

About

Brenda M. Rubenstein is the Vernon K. Krieble Professor of Chemistry, Professor of Physics, and Director of Data Science at Brown University. Her research interests encompass theoretical quantum chemistry and physics, stochastic methods for electronic structure theory, strongly correlated and relativistic materials, and alternative computing including quantum, molecular, neuromorphic, and biological computing. She is focused on developing electronic structure methods that are both highly accurate and scalable to enable theory-driven materials design, addressing the fundamental compromise in quantum chemistry between accuracy and computational speed. Rubenstein's group actively conducts research in molecular and quantum computing as well as computational biophysics. She earned her Ph.D. from Columbia University in 2013, her M.Phil. from the University of Cambridge in 2008, and her Sc.B. from Brown University in 2007. Her work aims to bridge the gap between modern experimental chemistry and quantum chemistry techniques, facilitating the analysis of complex molecules and materials at scales relevant to experimental questions. Rubenstein has contributed to advancing electronic structure methods and exploring innovative computational approaches, including the application of stochastic and alternative computing paradigms.

Research topics

• Computer Science
• Chemistry
• Materials science
• Condensed matter physics
• Database
• Computational biology
• Combinatorial chemistry
• Biochemistry
• Biology
• Nuclear magnetic resonance

Selected publications

• High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

  Nature Communications · 2024 · 179 citations

  Senior authorCorresponding

  This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative…

  DOI

• Multicomponent molecular memory

  Nature Communications · 2020 · 72 citations

  Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital d…

  PublisherOA PDFDOI

• Elucidating the Role of Dimensionality on the Electronic Structure of the Van der Waals Antiferromagnet NiPS₃

  Advanced Physics Research · 2024 · 15 citations

  Abstract The sustained interest in investigating magnetism in the 2D limit of insulating antiferromagnets is driven by the possibilities of discovering, or engineering, novel magnetic phases through layer stacking. However, due to the difficulty of directly measuring magnetic interactions in 2D antiferromagnets, it is not yet understood how intra layer magnetic interactions in insulating , strongly correlated, materials can be modified through layer proximity. Herein, the impact of reduced dimen…

  PublisherOA PDFDOI

• Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods

  Applied Physics Reviews · 2025-08-22 · 9 citations

  articleOpen access

  The field of 2D materials has grown dramatically in the past two decades. 2D materials can be utilized for a variety of next-generation optoelectronic, spintronic, clean energy, and quantum computing applications. These 2D structures, which are often exfoliated from layered van der Waals materials, possess highly inhomogeneous electron densities and can possess short- and long-range electron correlations. The complexities of 2D materials make them challenging to study with standard mean-field el…

  PublisherOA PDFDOI

• Quantum Computing Beyond Ground-State Electronic Structure: A Review of Progress Toward Quantum Chemistry Out of the Ground State

  Annual Review of Physical Chemistry · 2026-02-27 · 1 citations

  articleOpen accessSenior author

  Quantum computing offers the promise of revolutionizing quantum chemistry by enabling the solution of chemical problems for substantially less computational cost. While most demonstrations of quantum computation to date have focused on resolving the energies of the electronic ground states of small molecules, the field of quantum chemistry is far broader than ground-state chemistry; equally important to practicing chemists are chemical reaction dynamics and reaction mechanism prediction. Here, w…

  PublisherOA PDFDOI

Recent grants

• Beyond DFT: Accurate Simulations of Low Dimensional Materials For Energy and Device Applications

  NSF · $420k · 2018–2022

• CAREER: Finite Temperature Electronic Structure Methods for Predicting Material Phase Diagrams

  NSF · $650k · 2021–2026

Frequent coauthors

• Jacob K. Rosenstein

  Providence College

  60 shared

• Christopher Rose

  44 shared

• Christopher E. Arcadia

  Brown University

  40 shared

• Gabriel Monteiro da Silva

  37 shared

• Yuan Liu

  37 shared

• Sherief Reda

  36 shared

• Jordan Yang

  Brown University

  36 shared

• Eunsuk Kim

  Brown University

  34 shared

Labs

• The Rubenstein GroupPI

  Computational Chemistry at Brown University. We specialize in Quantum Chemistry, Biophysics, and Alternative Computing

Similar researchers at Brown University

• Shouheng Sunh-193
• Lai-Sheng Wangh-168
• Ming Xianh-70