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Kyle Bystrom

Showing results (1-10 of 4) with videos related to

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Journal of Chemical Theory and Computation|March 2, 2022
CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact ConstraintsKyle Bystrom, Boris Kozinsky
Journal of Chemical Theory and Computation|January 8, 2026
Size-Consistent Adiabatic Connection Functionals via Orbital-Based Matrix InterpolationKyle Bystrom, Timothy C Berkelbach
Journal of Chemical Theory and Computation|August 23, 2024
Training Machine-Learned Density Functionals on Band GapsKyle Bystrom, Stefano Falletta, Boris Kozinsky
The Journal of Physical Chemistry Letters|July 18, 2024
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic PotentialsZachary A H Goodwin, Malia B Wenny, Julia H Yang, et al.
Pageof 1

Showing results (1-10 of 4) with videos related to

Sort By:
Pageof 1
Journal of Chemical Theory and Computation|March 2, 2022
CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact ConstraintsKyle Bystrom, Boris Kozinsky
Journal of Chemical Theory and Computation|January 8, 2026
Size-Consistent Adiabatic Connection Functionals via Orbital-Based Matrix InterpolationKyle Bystrom, Timothy C Berkelbach
Journal of Chemical Theory and Computation|August 23, 2024
Training Machine-Learned Density Functionals on Band GapsKyle Bystrom, Stefano Falletta, Boris Kozinsky
The Journal of Physical Chemistry Letters|July 18, 2024
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic PotentialsZachary A H Goodwin, Malia B Wenny, Julia H Yang, et al.
Pageof 1