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Paul L. Houston

Chemistry Georgia Institute of Technology, Atlanta, GA 
photodissociation reactions and bimolecular reactions
"Paul Houston"

Mean distance: 8.12


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


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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
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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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