For decades, quantum chemists have been forced to make an oftentimes humbling choice in their day-to-day work: to use highly accurate, many-body methods that are too slow to apply to realistic quantum systems, or, to use faster one-body methods that are significantly less accurate. This fundamental compromise has glaringly limited the impact of quantum chemistry. Indeed, while most of modern experimental chemistry is focused upon synthesizing complex molecules and designing novel nano- and bulk materials, most modern quantum chemistry techniques are hard-pressed to even approach the scales necessary to answer many of the most pivotal experimental questions about these systems. The Rubenstein group is focused on developing electronic structure methods that are at once highly accurate and scale well with system size to help bridge this divide and enable theory-driven materials design. Other recurrent interests in the group revolve around molecular computing, quantum computing, computational biophysics, classical statistical mechanics, and computational linear algebra. 

Of key interest to the group is the development and application of stochastic methods to meet the long-standing need for rigorous techniques capable of accessing experimentally relevant scales. Because they make careful use of random numbers, quantum Monte Carlo methods are not only highly accurate (they are the gold standard in much of condensed matter physics), but scale gracefully with system size. They can therefore address a wide variety of experimentally motivated questions inaccessible to equally accurate deterministic methods and be used to improve existing electronic structure methods including Density Functional Theory. Problems of primary interest to the group fall into three main categories:

 

1. Modeling of bulk transition metal oxides, transition metal-based nanoclusters, and organometallic frameworks. Electrons in transition metals occupy highly degenerate, strongly-correlated d- and f- orbitals that are particularly difficult to model using one-body theories. The group’s recent advances developing stochastic techniques capable of modeling hundreds of electrons in hundreds of orbitals opens the door to simulating a variety of transition metal-based compounds and materials. Quantum Monte Carlo simulations of transition metal oxides will enable the high accuracy prediction of band gaps and high temperature, high pressure phase transitions, which will pave the way toward the development of more advanced photovoltaics as well as an improved understanding of the phase diagrams of complex solids.

2. Development of new methods capable of capturing relativistic effects, including spin-orbit coupling, beyond the mean field level. Being able to properly model relativistic effects is critical to predicting the behavior of heavy elements including Se and Te, which comprise topological insulators and important semiconductors, and the lanthanides and actinides, which assume a pivotal role in the nuclear fuel cycle and exhibit intriguing volume collapse transitions in the bulk. Our group is currently exploiting quantum Monte Carlo’s unique ability to obtain the ground state wave functions of multi-component Hamiltonians to explore the full hierarchy of relativistic Hamiltonians and their accuracy in quantum chemical calculations. These efforts will inform current experimental efforts underway at Brown and elsewhere.

3. Design and testing of finite temperature electronic approaches to model hot electrons and astrophysical conditions. Few electronic structure approaches are capable of modeling materials under conditions in which electrons are excited well above the ground state. This currently limits our understanding of photochemistry, plasma physics, and astrophysical processes such as star formation. Our group is developing density matrix-based methods that will enable the direct prediction of molecular behavior at high temperatures using both stochastic methods and more conventional finite temperature density functional theories.

Advances made in these directions will establish the foundations necessary for rigorous simulations of a variety of materials currently outside the scope of modern-day exploration, dramatically expanding the predictive power of quantum chemical techniques. Students engaged in this work will develop a deep understanding of both quantum and statistical mechanics and will become familiar with modern high performance computing paradigms.

The Rubenstein Group has also had a long-standing interest in biophysical modeling and alternative computing technologies, including quantum, molecular, and biological computation. 

Air Force Office of Scientific Research DEPSCoR Collaboration Grant, "Probing Electron Nematicity in Multilayer Graphene Heterostructures (Co-PI w/ Jia Li)," 2023-2026. 

Cottrell Collaborative Grant, "Broadening and Deepening the ESCIP Network: Infusing Computational Science Concepts into STEM Courses through Multidisciplinary Instructor Collaborative Networks (Co-PI)," 2022-2024. 

Cottrell Collaborative Grant, "LABSIP: Lowering the Activation Barrier to Success in PChem (Co-PI)," 2022-2024. 

Camille Dreyfus Teacher-Scholar Award, "Accurate and Efficient Stochastic Electronic Structure Algorithms for Materials Design (PI)," 2021-2026. 

NSF CAREER Award, "Finite Temperature Electronic Structure Algorithms for Predicting Material Phase Diagrams (PI)," 2021-2026. 

NSF SemiSynthBio Program, "Hybrid Biofilm Semiconductor Information Systems (Co-PI)," 2020-2024.

Cottrell Teacher-Scholar Award, "Catalyst Design via Data-Enabled Quantum Chemistry and Integrating Data Science into the Chemistry Curriculum (PI)," 2020-2023. 

Air Force Office of Scientific Research Young Investigator Award, "Stochastic Methods for Carbon Dioxide Catalysis (PI)," 2020-2023. 

DOE CCMS Center, "Center for Predictive Simulations of Functional Materials (Co-PI w/ Paul Kent, Jaron Krogel, Luke Shulenberger, Anouar Benali, and Lubos Mitas)," 2019-2024.

NSF Eager Grant, "Synthetic Chemical-Based Information Processing (Co-PI w/ Sherief Reda, Jacob Rosenstein, and Eunsuk Kim)," 2019-2022. 

NSF EPSCoR RII Track-2 FEC Grant, "Harnessing the Data Revolution for the Quantum Leap: From Quantum Control to Quantum Materials (Co-I w/Vesna Mitrovic, Brad Marston, Dmitri Feldman, and Chandresekhar Ramanathn)," 2019-2024. 

NSF Major Research Instrumentation Grant, "Acquisition of a Maskless Lithography Tool for the Brown Nanofabrication Central Facility (Co-PI w/ Alexander Zaslavsky, Anita Skukla, Domenico Pacifici, and Derek Stein)," 2019-Present

Alfred P. Sloan Fellowship, "Quantum Chemistry That Scales," 2019-Present

DOE Computational Chemical Science Research Center, "Bridging the Time Scale in Exascale Computing of Chemical Systems (Co-I w/ Andrew Peterson, Franklin Goldsmith, Zachary Ulissi, Andrew Medford, and Matthew Willard)," 2018-Present 

NSF DMR, "Beyond DFT: Accurate Simulations of Low Dimensional Materials for Energy and Device Applications (PI w/ Can Ataca)," 2018-Present

DARPA Molecular Informatics Program, "Chemical CPUs: Chemical Computational Processing via Ugi Reactions (PI w/ Jacob Rosenstein, Chris Rose, Peter Weber, Sherief Reda, Eunsuk Kim, Joseph Geiser, and Jason Sello)," 2017-Present

NSF EPSCoR Program, "Genotypes to Phenoytpes (w/ Marty Ytreberg, Daniel Weinreich, Holly Wichman, others)," 2017-2021

Defense Science Study Group Selection, 2023
Fulbright Senior Scholar, 2023
Meenakshi Narrain Award for Undergraduate Research Mentoring, 2023
Popular Science's Brilliant Ten, 2021
Camille Dreyfus Teacher-Scholar Award, 2021
Cottrell Teacher-Scholar Award, 2020
Chemical and Engineering News Talented Twelve, 2019
Brown University DIAP Faculty Award, 2019
Alfred P. Sloan Fellow, 2019
Dell-Intel Young Investigator Award in Quantum Chemistry, 2018
Lawrence Distinguished Postdoctoral Fellowship, 2013-2016
National Science Foundation Graduate Research Fellowship, 2008-2013
Department of Energy Computational Science Graduate Fellowship, 2008-2012
Winston Churchill Foundation of America Fellowship to Cambridge, 2007-2008
Paul Cross Prize for Best Senior in Physical Chemistry (Brown), 2007
Leallyn B. Clapp Thesis Prize for Best Thesis in Physical Chemistry (Brown), 2007
Rhodes Scholarship Finalist (NJ/MA Region), 2006
Barry Goldwater Scholarship (NJ Region), 2006-2007
 

American Chemical Society
American Physical Society
Biophysical Society
Materials Research Society

I am passionate about teaching introductory chemistry as well as a variety of undergraduate and graduate physical chemistry courses, including General Chemistry, Thermodynamics, Statistical Mechanics, Quantum Chemistry, Quantum Computing, and Computational Chemistry. 

I am even more passionate about level the educational playing field and increasing socioeconomic, racial, and gender diversity in STEM.