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James S Spencer

Showing results (1-10 of 9) with videos related to

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The Journal of Chemical Physics|March 3, 2016
Developments in stochastic coupled cluster theory: The initiator approximation and application to the uniform electron gasJames S Spencer, Alex J W Thom
Journal of Chemical Theory and Computation|December 12, 2022
ipie: A Python-Based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUsFionn D Malone, Ankit Mahajan, James S Spencer, et al.
Nature Communications|June 18, 2024
Neural network variational Monte Carlo for positronic chemistryGino Cassella, W M C Foulkes, David Pfau, et al.
Science (New York, N.Y.)|August 22, 2024
Accurate computation of quantum excited states with neural networksDavid Pfau, Simon Axelrod, Halvard Sutterud, et al.
Physical Review Letters|February 10, 2023
Discovering Quantum Phase Transitions with Fermionic Neural NetworksGino Cassella, Halvard Sutterud, Sam Azadi, et al.
Science (New York, N.Y.)|August 4, 2022
Response to Comment on "Pushing the frontiers of density functionals by solving the fractional electron problem"James Kirkpatrick, Brendan McMorrow, David H P Turban, et al.
Science (New York, N.Y.)|December 9, 2021
Pushing the frontiers of density functionals by solving the fractional electron problemJames Kirkpatrick, Brendan McMorrow, David H P Turban, et al.
Journal of Chemical Theory and Computation|January 26, 2019
The HANDE-QMC Project: Open-Source Stochastic Quantum Chemistry from the Ground State UpJames S Spencer, Nick S Blunt, Seonghoon Choi, et al.
Nature|July 8, 2026
Reinforcement learning control of quantum error correctionVolodymyr Sivak, Alexis Morvan, Michael Broughton, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
The Journal of Chemical Physics|March 3, 2016
Developments in stochastic coupled cluster theory: The initiator approximation and application to the uniform electron gasJames S Spencer, Alex J W Thom
Journal of Chemical Theory and Computation|December 12, 2022
ipie: A Python-Based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUsFionn D Malone, Ankit Mahajan, James S Spencer, et al.
Nature Communications|June 18, 2024
Neural network variational Monte Carlo for positronic chemistryGino Cassella, W M C Foulkes, David Pfau, et al.
Science (New York, N.Y.)|August 22, 2024
Accurate computation of quantum excited states with neural networksDavid Pfau, Simon Axelrod, Halvard Sutterud, et al.
Physical Review Letters|February 10, 2023
Discovering Quantum Phase Transitions with Fermionic Neural NetworksGino Cassella, Halvard Sutterud, Sam Azadi, et al.
Science (New York, N.Y.)|August 4, 2022
Response to Comment on "Pushing the frontiers of density functionals by solving the fractional electron problem"James Kirkpatrick, Brendan McMorrow, David H P Turban, et al.
Science (New York, N.Y.)|December 9, 2021
Pushing the frontiers of density functionals by solving the fractional electron problemJames Kirkpatrick, Brendan McMorrow, David H P Turban, et al.
Journal of Chemical Theory and Computation|January 26, 2019
The HANDE-QMC Project: Open-Source Stochastic Quantum Chemistry from the Ground State UpJames S Spencer, Nick S Blunt, Seonghoon Choi, et al.
Nature|July 8, 2026
Reinforcement learning control of quantum error correctionVolodymyr Sivak, Alexis Morvan, Michael Broughton, et al.
Pageof 1