Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Nicole DeGregorio

Showing results (1-10 of 5) with videos related to

Pageof 1
Sort By:
Journal of Chemical Theory and Computation|April 20, 2019
Adaptive Dimensional Decoupling for Compression of Quantum Nuclear Wave Functions and Efficient Potential Energy Surface Representations through Tensor Network DecompositionNicole DeGregorio, Srinivasan S Iyengar
Faraday Discussions|October 9, 2019
Challenges in constructing accurate methods for hydrogen transfer reactions in large biological assemblies: rare events sampling for mechanistic discovery and tensor networks for quantum nuclear effectsNicole DeGregorio, Srinivasan S Iyengar
Journal of Chemical Theory and Computation|November 29, 2017
Efficient and Adaptive Methods for Computing Accurate Potential Surfaces for Quantum Nuclear Effects: Applications to Hydrogen-Transfer ReactionsNicole DeGregorio, Srinivasan S Iyengar
Journal of Chemical Theory and Computation|October 8, 2021
Graph-Theory-Based Molecular Fragmentation for Efficient and Accurate Potential Surface Calculations in Multiple DimensionsAnup Kumar, Nicole DeGregorio, Srinivasan S Iyengar
Journal of Chemical Theory and Computation|November 4, 2022
Graph-Theoretic Molecular Fragmentation for Potential Surfaces Leads Naturally to a Tensor Network Form and Allows Accurate and Efficient Quantum Nuclear DynamicsAnup Kumar, Nicole DeGregorio, Timothy Ricard, et al.
Pageof 1

Showing results (1-10 of 5) with videos related to

Sort By:
Pageof 1
Journal of Chemical Theory and Computation|April 20, 2019
Adaptive Dimensional Decoupling for Compression of Quantum Nuclear Wave Functions and Efficient Potential Energy Surface Representations through Tensor Network DecompositionNicole DeGregorio, Srinivasan S Iyengar
Faraday Discussions|October 9, 2019
Challenges in constructing accurate methods for hydrogen transfer reactions in large biological assemblies: rare events sampling for mechanistic discovery and tensor networks for quantum nuclear effectsNicole DeGregorio, Srinivasan S Iyengar
Journal of Chemical Theory and Computation|November 29, 2017
Efficient and Adaptive Methods for Computing Accurate Potential Surfaces for Quantum Nuclear Effects: Applications to Hydrogen-Transfer ReactionsNicole DeGregorio, Srinivasan S Iyengar
Journal of Chemical Theory and Computation|October 8, 2021
Graph-Theory-Based Molecular Fragmentation for Efficient and Accurate Potential Surface Calculations in Multiple DimensionsAnup Kumar, Nicole DeGregorio, Srinivasan S Iyengar
Journal of Chemical Theory and Computation|November 4, 2022
Graph-Theoretic Molecular Fragmentation for Potential Surfaces Leads Naturally to a Tensor Network Form and Allows Accurate and Efficient Quantum Nuclear DynamicsAnup Kumar, Nicole DeGregorio, Timothy Ricard, et al.
Pageof 1