Publications

Rubenstein Group Publications

Journal Articles, and Refereed Book Chapters

T. Shen, H. Barghathi, J. Yu, A. Del Maestro, B.M. Rubenstein. Stable Recursive Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble: Applications to Thermometry and the Hubbard Model. Accepted to Phys. Rev. E. (2023). https://arxiv.org/abs/2212.08654

A. Agiza, K. Oakley, J. Rosenstein, B.M. Rubenstein, E. Kim, M. Riedel, and S. Reda. Digital Circuits and Neural Networks Based on Acid-Base Chemistry Implemented by Robotic Fluid Handling. Accepted at Nature Communications (2023). 

C. Huang and B. Rubenstein. Machine Learning Diffusion Monte Carlo Forces. Accepted to the Journal of Physical Chemistry A (2022). https://arxiv.org/abs/2211.07103

S. Li, J. Patel, J. Yang, A. Crabtree, B. Rubenstein, P. Lund-Andersen, F. Ytreberg, and P. Rowley. Defining the HIV Capsid Binding Site of Nucleoporin 153. Accepted to mSphere (2022). https://www.biorxiv.org/content/10.1101/2022.05.06.490988v1

G. Iyer and B.M. Rubenstein. Error Cancellation in Diffusion Monte Carlo Calculations of Surface Chemistry. Accepted for J. Phys. Chem. C (2022). arXiv:2206.00729.

G. Monteiro da Silva, J. Yang, B. Leang, J. Huang, D.M. Weinreich, and B.M. Rubenstein. Covalent Docking and Molecular Dynamics Simulations Reveal the Specificity-Shifting Mutations Ala237Arg and Ala237Lys in TEM Beta-Lactamase. PLoS Comput. Biol. 18(6): e1009944 (2022). BioRXiv: 2022.04.29.490038v1. 

W. S. Tang, G. Monteiro da Silva, H. Kirveslahti, E. Skeens, B. Feng, T. Sudijono, K. Yang, S. Mukherjee, B. Rubenstein, and L. Crawford (Co First Authors). A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics. PLoS Comput Biol. 18(5): e1010045 (2022). BioRXiv: 2021.07.28.454240v1. 

B. Foulon,  K. Ray, C. Kim, Y. Liu, B. M. Rubenstein, and V. Lordi (Dual Corresponding Authors). 1/w Electric-Field Noise in Surface Ion Traps from Correlated Adsorbate Dynamics. Phys. Rev. A, 105, 013107 (2022). arXiv:2107.01177. 

J. Lai,  J. Yang, E. Uzun, B.M. Rubenstein, and I. N. Sarkar. LYRUS: A Machine Learning Model for Predicting the Pathogenicity of Missense Variants. Bioinformatics Advances, 2(1): vbab045 (2022). bioRXiv:10.1101/2021.05.10.443497v1.

D. Staros, G. Hu, R. Nanguneri, J. Krogel, M.C. Bennett, O. Heinonen, P. Ganesh, and B.M. Rubenstein. A Combined First Principles Study of the Structural, Magnetic, and Phonon Properties of Monolayer CrI3. J. Chem. Phys. 156, 014707 (2022). arXiv:2110.06731. 

E. Kennedy, J. Geiser, C. Arcadia, P. Weber, C. Rose, B.M. Rubenstein, and J.K. Rosenstein. Secret Messaging with Endogenous Chemistry. Scientific Reports, 11, 13960 (2021).

M.S. Church and B.M. Rubenstein. Real Time Dynamics of Correlated Fermions via Auxiliary Field Quantum Monte Carlo. J. Chem. Phys., 154, 184103 (2021).

A. Dombroski, K. Oakley, C. Arcadia, F. Nouraei, S.L. Chen, C. Rose, B. M. Rubenstein, J. Rosenstein, S.Reda, and E. Kim. Implementing Parallel Arithmetic via Acetylation and Its Application to Chemical Image Processing. Proceedings of the Royal Society A, 477, 2248 (2021). https://royalsocietypublishing.org/doi/10.1098/rspa.2020.0899

C. Arcadia, A. Dombroski, K. Oakley, S.-L. Chen, H. Tann, C. Rose, E. Kim, S. Reda, B. M. Rubenstein, and J. Rosenstein. Leveraging Autocatalytic Reactions for Chemical-Domain Image Classification. Chemical Science. (2021). pubs.rsc.org/en/Content/ArticleLanding/2021/SC/D0SC05860B#!divAbstract

Related Press
https://www.chemistryworld.com/news/neural-network-based-on-autocatalytic-reaction-performs-image-classification/4013413.article

Y. Liu, G.-Z. Zhu, D.-F., Yuan, C.-H. Qian, Y.-R. Zhang, B. M. Rubenstein, and L.-S. Wang. Observation of a Symmetry-Forbidden Excited Quadrupole-Bound State. JACS, 142 (47), 20240-20246 (2020). https://pubs.acs.org/doi/10.1021/jacs.0c10552

T. Shen, Y. Liu, Y. Yu, and B. M. Rubenstein. Finite Temperature Auxiliary Field Quantum Monte Carlo in the Canonical Ensemble​. J. Chem. Phys. 153, 204108 (2020). https://aip.scitation.org/doi/10.1063/5.0026606

D. Yuan, Y. Liu, C.-H. Qian, G. S. Kocheril, Y.-R. Zhang, B. M. Rubenstein, and L.-S. Wang. Polarization of Valence Orbitals by the Intramolecular Electric Field from a Diffuse Dipole-Bound Electron. J. Phys. Chem. Lett. 1 (18), 7914–7919 (2020). https://pubs.acs.org/doi/10.1021/acs.jpclett.0c02514

D. Yuan, Y. Liu, C.-H. Qian, Y.-R. Zhang, B. M. Rubenstein, and L.-S. Wang. Observation of a  Pi-Type Dipole-Bound State in Molecular Anions. Phys. Rev. Lett. 125, 073003 (2020). https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.073003

Y. Liu, T. Shen, H. Zhang, and B. M. Rubenstein. Unveiling the Finite Temperature Physics of Hydrogen Chains via Auxiliary Field Quantum Monte Carlo. Journal of Chemical Theory and Computation. 16 (7), 4298-4314 (2020). https://pubs.acs.org/doi/10.1021/acs.jctc.0c00288​. https://arxiv.org/abs/2004.01194

J. Yang, N. Naik, J. S. Patel, C. S. Wylie, W. Gu, J. Huang, F. M. Ytreberg, M. T. Naik, D. M. Weinreich, and B. M. Rubenstein. Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness​. PLOS One. 15(5), e0233509 (2020). https://doi.org/10.1371/journal.pone.0233509 https://www.biorxiv.org/content/10.1101/2020.04.15.043661v1

H. Hao, A. Georges, A. Millis, B.M. Rubenstein, Q. Han, and H. Shi. Metal-Insulator and Magnetic Phase Diagram of Ca2RuO4 from Auxiliary Field Quantum Monte Carlo and Dynamical Mean Field Theory. Physical Review B, 101, 235110 (2020). https://doi.org/10.1103/PhysRevB.101.235110​ https://arxiv.org/abs/1911.02702

R. Cong, R. Nanguneri, B.M. Rubenstein, and V. Mitrovic. First Principles Calculations of the EFG Tensors of Ba2NaOsO6, a Mott Insulator with Strong Spin Orbit Coupling. Journal of Physics.: Condensed Matter, 100, 245141 (2020). https://arxiv.org/abs/1908.09014

P. R. C. Kent, A. Annaberdiyev, A. Benali, M. Chandler Bennett, E. J. Landinez Borda, P. Doak, K. D. Jordan, J. T. Kogel, I. Kylanpaa, J. Lee, Y. Luo, F. D. Malone, C. A. Melton, L. Mitas, M. A. Morales, E. Neuscamman, F. A. Reboredo, B. Rubenstein, K. Saritas, S. Upadhyay, H. Hao, G. Wang, S. Zhang, and L. Zhao. QMCPACK: Advances in the Development, Efficiency, and Application of Auxiliary Field and Real-Space Variational and Diffusion Quantum Monte Carlo. Selected as the Editor's Pick by the Journal of Chemical Physics. 152 (17): 174105 (2020). https://arxiv.org/abs/2003.01831; https://aip.scitation.org/doi/10.1063/5.0004860

J. K. Rosenstein, C. Rose, S. Reda, P. M. Weber, E. Kim, J. Sello, J. Geiser, E. Kennedy, C. Arcadia, A. Dombroski, K. Oakley, S.-L. Chen, H. Tann, and B. M. Rubenstein. Principles of Information Storage in Small-Molecular Mixtures. Accepted for IEEE Transactions on Nanobioscience (2020). https://ieeexplore.ieee.org/document/9018156

C. E. Arcadia, E. Kennedy, J. Geiser, A. Dombroski, K. Oakley, S.-L. Chen, L. Sprague, M. Ozmen, J. Sello, P. M. Weber, S. Reda, C. Rose, E. Kim, B. M. Rubenstein, and J. K. Rosenstein. Multicomponent Molecular Memory. Nature Communications (2020). https://www.nature.com/articles/s41467-020-14455-1

Related Press:
https://bioengineeringcommunity.nature.com/users/345076-jacob-k-rosenstein/posts/59230-multicomponent-molecular-memory
https://www.sciencedaily.com/releases/2020/02/200204163650.htm
https://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=300015&org=NSF&from=news

B. Foulon, Y. Liu, J. K. Rosenstein, and B. M. Rubenstein. A Language for Molecular Computation. Chem (2019). https://www.sciencedirect.com/science/article/pii/S2451929419305170?dgcid=author

R. Cong, R. Nanguneri, B. M. Rubenstein, and V. Mitrovic. First Principles Calculations of the EFG Tensors of Ba2NaOsO6, a Mott Insulator with Strong Spin Orbit Coupling. Accepted for Physical Review B, 100, 245141 (2019). https://arxiv.org/abs/1908.09014 ; https://journals.aps.org/prb/abstract/10.1103/PhysRevB.100.245141

L. Sprague, C. Huang, J.-P. Song, and B. M. Rubenstein. Maximizing Thermoelectric Figures of Merit by Uniaxially Straining Indium Selenide. Journal of Physical Chemistry C (2019). https://pubs.acs.org/doi/full/10.1021/acs.jpcc.9b05681

E. Kennedy, C. Arcadia, J. Geiser, P. M. Weber, C. Rose, B.M. Rubenstein, and J. Rosenstein. Encoding Information in Synthetic Metabolomes. PLOS One (2019). https://www.biorxiv.org/content/10.1101/627745v1

Related Press:
New Scientist Magazine - https://www.newscientist.com/article/2208439-data-can-now-be-stored-inside-the-molecules-that-power-our-metabolism/
The Naked Scientist Interview - https://www.thenakedscientists.com/articles/science-news/molecular-data-storage
Phys.Org Press - https://www.brown.edu/news/2019-07-03/molecules
Nature Highlight - https://www.nature.com/articles/d41586-019-02070-0

K.G. Ray,* B.M. Rubenstein,* W. Gu, and V. Lordi. vdW-Corrected Density Functional Study of Electric Field Noise Heating in Ion Traps Caused by Electrode Surface Adsorbates. New J. Phys. (2019); https://iopscience.iop.org/article/10.1088/1367-2630/ab1875, arXiv:1810.10199. *Authors contributed equally to this work.

H. Hao, B.M. Rubenstein, and H. Shi. Auxiliary Field Quantum Monte Carlo for Multiband Hubbard Models: Controlling the Sign and Phase Problems to Capture Hund's Physics. Phys. Rev. B (2019). https://arxiv.org/abs/1902.01463 https://journals.aps.org/prb/abstract/10.1103/PhysRevB.99.235142

H. Hao, J. Shee, S. Upadhyay, C. Ataca, K. Jordan, and B.M. Rubenstein. Accurate Predictions of Electron Binding Energies of Dipole-Bound Anions via Quantum Monte Carlo Methods. Journal of Chemical Physics Letters (2018). https://arxiv.org/abs/1809.09771 https://pubs.acs.org/doi/10.1021/acs.jpclett.8b02733. 

T. Cai, H. Yang, K. Hills-Kimball, J.-P. Song, H. Zhu, E. Hofamn, W. Zheng, B.M. Rubenstein, and O. Chen. Synthesis of All-Inorganic Cd-Doped CsPbCl3 Perovskite Nanocrystals with Dual-Wavelength Emission. Journal of Chemical Physics Letters (2018). https://pubs.acs.org/doi/10.1021/acs.jpclett.8b03412

C. Arcadia, H. Tann, A. Dombroski, K. Ferguson, S.-L. Chen, E. Kim, C. Rose, B.M. Rubenstein, S. Reda, and J. Rosenstein. Parallelized Linear Classification with Volumetric Chemical Perceptrons. Accepted to Proceedings of the International Conference on Rebooting Computing (2018). http://scale.engin.brown.edu/pubs/ICRC18.pdf

Y. Liu, M. Cho, and B.M. Rubenstein. Finite Temperature Ab Initio Auxiliary Field Quantum Monte Carlo. Journal of Chemical Theory and Computation (2018). https://pubs.acs.org/doi/10.1021/acs.jctc.8b00569 https://arxiv.org/abs/1806.02848

H. Zhu, T. Cai, M. Que, J.-P. Song, B.M. Rubenstein, Z. Wang, and O. Chen. Pressure-Induced Phase Transformation and Band-Gap Engineering of Formamidinium Lead Iodide Perovskite Nanocrystals. Journal of Physical Chemistry Letters, 9(15), 4199 (2018). https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.8b01852

J. Kim et al. QMCPACK: An Open Source Ab Initio Quantum Monte Carlo Package for the Electronic Structure of Atoms, Molecules, and Solids. Journal of Physics: Condensed Matter, 30, 195901 (2018). http://iopscience.iop.org/article/10.1088/1361-648X/aab9c3/meta https://arxiv.org/abs/1802.06922

C. Rose, S. Reda, B.M. Rubenstein, and J. Rosenstein. Computing with Chemicals: Perceptrons Using Small Molecules. 2018 IEEE International Symposium on Information Theory (ISIT). https://ieeexplore.ieee.org/abstract/document/8437814/

M. Gulian, H. Yang, and B.M. Rubenstein. Fractional Path Integral Monte Carlo. Submitted to JCP (2017). https://arxiv.org/abs/1709.09089

B.M. Rubenstein. Introduction to the variational Monte Carlo method in quantum chemistry and physics. Variational Methods in Molecular Modeling, ed. Jianzhong Wu (2016). http://link.springer.com/book/10.1007/978-981-10-2502-0. 

C.-C. Chang, B.M. Rubenstein, and M.A. Morales-Silva. Auxiliary-field based trial wave functions in quantum Monte Carlo calculations. Physical Review B (Rapid). 94, 235144 (2016). arXiv:1604.00345.

Before Independent Career
B. M. Rubenstein, S. Zhang, and D.R. Reichman. Finite-temperature auxiliary-field quantum Monte Carlo technique for Bose-Fermi mixtures. Physical Review A. 86, 053606 (2012). arXiv:1209.0186. 

B.M. Rubenstein, I. Coluzza, and M.A. Miller. Controlling the folding and substrate-binding of proteins using polymer brushes. Physical Review Letters. 108, 208104 (2012). arXiv:1204.6177.

B.M. Rubenstein, J.E. Gubernatis, and J.D. Doll. Comparative Monte Carlo efficiency by Monte Carlo analysis. Physical Review E. 82, 036701 (2010). arXiv:1004.0931v1. 

B.M. Rubenstein and L. J. Kaufman. The role of extracellular matrix in glioma invasion: A cellular Potts model approach. Biophysical Journal. 95(12): 5661 (2008).

Books

E. Schwegler, B. Rubenstein, and S. Libby (Editors). Symposium in Honor of Dr. Berni Alder's 90th Birthday. Advances in the Computational Sciences. World Scientific (2017). http://www.worldscientific.com/worldscibooks/10.1142/10432.

Theses

B.M. Rubenstein. Sc.B. Thesis (Brown University), Complex Structure, Complex Dynamics: The Dynamics of Liquid Crystals in the Nematic Phase (2007).

B.M. Rubenstein. M.Phil. Thesis (University of Cambridge), Protein Folding and Binding Amidst Entropy Sources (2008).

B.M. Rubenstein. Ph.D. Thesis (Columbia University), Novel Quantum Monte Carlo Approaches for Quantum Liquids (2013).

PATENTS

B.M. Rubenstein and J. Rosenstein. Encoding Information in Synthetic Metabolomes and Molecular Mixtures. Provisional Patent Application (2018).

B.M. Rubenstein, J. Rosenstein, E. Kim, S. Reda, J.D. Geiser, P.M. Weber, C. Rose, and J. Sello. Methods of Chemical Computation, Patent Application (Provisional 2018, Full 2019).