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Paul L. Houston

Affiliations:

Chemistry

Georgia Institute of Technology, Atlanta, GA

Area:

photodissociation reactions and bimolecular reactions

Website:

http://www.chemistry.gatech.edu/faculty/Houston/

Google:

Bio:

http://www.cosbkup.gatech.edu/group/PLHVitae.htm

Mean distance: 8.12

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Parents

Jeffrey I. Steinfeld	grad student	1973	MIT
(Infrared-infrared double resonance)
C. Bradley Moore	post-doc	1973-1975	UC Berkeley

Children

George McBane	grad student		Grand Valley State University
Robert J. Hamers	grad student	1986	Cornell
Vincent Hradil	grad student	1986-1992	Cornell
Michael S. Westley	grad student	2000	Cornell
Scott M. Dylewski	grad student	2001	Cornell
Stephen Gomez Diaz	grad student	2001	Cornell
Jason A. Barron	grad student	2002	Cornell
Bogdan R. Cosofret	grad student	2003	Cornell
Jennifer M. Gaudioso	grad student	2003	Cornell
Onur Tokel	grad student	2011	Cornell
Scott Henderson Kable	post-doc		UNSW Australia
Vartkess Ara Apkarian	post-doc	1981-1983	Cornell
Arthur Suits	post-doc	1991-1993	Cornell
Michael A. Carpenter	post-doc	1996-1998	Cornell
Benjamin J Whitaker	research scientist	1988-1989

Collaborators

collaborator

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Publications

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Ge F, Wang R, Qu C, et al. (2024) Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine. The Journal of Physical Chemistry Letters. 4451-4460
Houston PL, Qu C, Yu Q, et al. (2024) No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials. Journal of Chemical Theory and Computation
Houston PL, Qu C, Yu Q, et al. (2024) Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer. Journal of Chemical Theory and Computation
Pandey P, Qu C, Nandi A, et al. (2024) Ab Initio Potential Energy Surface for NaCl-H with Correct Long-Range Behavior. The Journal of Physical Chemistry. A
Houston PL, Qu C, Yu Q, et al. (2024) A New Method to Avoid Calculation of Negligible Hamiltonian Matrix Elements in CI Calculation. The Journal of Physical Chemistry. A
Yu Q, Qu C, Houston PL, et al. (2023) A Status Report on "Gold Standard" Machine-Learned Potentials for Water. The Journal of Physical Chemistry Letters. 8077-8087
Qu C, Houston PL, Yu Q, et al. (2023) Machine learning classification can significantly reduce the cost of calculating the Hamiltonian matrix in CI calculations. The Journal of Chemical Physics. 159
Qu C, Yu Q, Houston PL, et al. (2023) Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds. Journal of Chemical Theory and Computation
Nandi A, Laude G, Khire SS, et al. (2023) Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands. Journal of the American Chemical Society. 145: 9655-9664
Houston PL, Qu C, Yu Q, et al. (2023) PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials. The Journal of Chemical Physics. 158: 044109

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