David R. Reichman
Affiliations: | Chemistry | Columbia University, New York, NY |
Area:
chemistry, physics, and biology of disordered materialsWebsite:
http://www.columbia.edu/cu/chemistry/groups/reichman/people/dave.htmlGoogle:
"David Reichman"Bio:
http://www.columbia.edu/cu/chemistry/fac-bios/reichman/faculty.html
Mean distance: 7.88 | S | N | B | C | P |
Parents
Sign in to add mentorRobert J. Silbey | grad student | 1997 | MIT | |
(Relaxation and spectra in condensed phases) | ||||
Gregory A. Voth | post-doc | 1997-1999 | University of Utah |
Children
Sign in to add traineeCollaborators
Sign in to add collaboratorEran Rabani | collaborator | ||
Benjamin Rudshteyn | collaborator | 2018-2021 | Columbia |
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Publications
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Mejía L, Yin J, Reichman DR, et al. (2023) Stochastic Real-Time Second-Order Green's Function Theory for Neutral Excitations in Molecules and Nanostructures. Journal of Chemical Theory and Computation |
Babbush R, Huggins WJ, Berry DW, et al. (2023) Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods. Nature Communications. 14: 4058 |
Xu D, Mandal A, Baxter JM, et al. (2023) Ultrafast imaging of polariton propagation and interactions. Nature Communications. 14: 3881 |
Lindoy LP, Mandal A, Reichman DR. (2023) Quantum dynamical effects of vibrational strong coupling in chemical reactivity. Nature Communications. 14: 2733 |
Mandal A, Xu D, Mahajan A, et al. (2023) Microscopic Theory of Multimode Polariton Dispersion in Multilayered Materials. Nano Letters |
Shee J, Weber JL, Reichman DR, et al. (2023) On the potentially transformative role of auxiliary-field quantum Monte Carlo in quantum chemistry: A highly accurate method for transition metals and beyond. The Journal of Chemical Physics. 158: 140901 |
Lee S, Lee J, Zhai H, et al. (2023) Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry. Nature Communications. 14: 1952 |
Ceriotti M, Jensen L, Manolopoulos DE, et al. (2023) 2021 JCP Emerging Investigator Special Collection. The Journal of Chemical Physics. 158: 060401 |
Chen MS, Lee J, Ye HZ, et al. (2023) Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy. Journal of Chemical Theory and Computation |
Bowman GR, Cox SJ, Dellago C, et al. (2022) Remembering the Work of Phillip L. Geissler: A Coda to His Scientific Trajectory. Annual Review of Physical Chemistry |