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The Journal of Chemical Physics
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October 23, 2017
Comment on "A new parametrizable model of molecular electronic structure" [J. Chem. Phys. 135, 134120 (2011)]
Ksenia R Briling
The Journal of Chemical Physics
|
July 16, 2021
Impact of quantum-chemical metrics on the machine learning prediction of electron density
Ksenia R Briling, Alberto Fabrizio, Clemence Corminboeuf
Digital Discovery
|
June 30, 2022
SPA<sup>H</sup>M: the spectrum of approximated Hamiltonian matrices representations
Alberto Fabrizio, Ksenia R Briling, Clemence Corminboeuf
The Journal of Physical Chemistry Letters
|
June 22, 2021
Learning the Exciton Properties of Azo-dyes
Sergi Vela, Alberto Fabrizio, Ksenia R Briling, et al.
Journal of Chemical Theory and Computation
|
January 16, 2024
SPA<sup>H</sup>M(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations
Ksenia R Briling, Yannick Calvino Alonso, Alberto Fabrizio, et al.
The Journal of Chemical Physics
|
December 2, 2020
Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation
Alberto Fabrizio, Ksenia R Briling, David D Girardier, et al.
Digital Discovery
|
May 11, 2026
Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states
Yuri Cho, Ksenia R Briling, Yannick Calvino Alonso, et al.
Digital Discovery
|
May 17, 2024
Benchmarking machine-readable vectors of chemical reactions on computed activation barriers
Puck van Gerwen, Ksenia R Briling, Yannick Calvino Alonso, et al.
Journal of Chemical Information and Modeling
|
July 15, 2024
3DReact: Geometric Deep Learning for Chemical Reactions
Puck van Gerwen, Ksenia R Briling, Charlotte Bunne, et al.
Faraday Discussions
|
December 18, 2024
Discovering synthesis targets: general discussion
Andy S Anker, Alán Aspuru-Guzik, Tim Bechtel, et al.
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of 2
Search research articles
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Showing results (1-10 of 12) with videos related to
Sort By:
Page
of 2
The Journal of Chemical Physics
|
October 23, 2017
Comment on "A new parametrizable model of molecular electronic structure" [J. Chem. Phys. 135, 134120 (2011)]
Ksenia R Briling
The Journal of Chemical Physics
|
July 16, 2021
Impact of quantum-chemical metrics on the machine learning prediction of electron density
Ksenia R Briling, Alberto Fabrizio, Clemence Corminboeuf
Digital Discovery
|
June 30, 2022
SPA<sup>H</sup>M: the spectrum of approximated Hamiltonian matrices representations
Alberto Fabrizio, Ksenia R Briling, Clemence Corminboeuf
The Journal of Physical Chemistry Letters
|
June 22, 2021
Learning the Exciton Properties of Azo-dyes
Sergi Vela, Alberto Fabrizio, Ksenia R Briling, et al.
Journal of Chemical Theory and Computation
|
January 16, 2024
SPA<sup>H</sup>M(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations
Ksenia R Briling, Yannick Calvino Alonso, Alberto Fabrizio, et al.
The Journal of Chemical Physics
|
December 2, 2020
Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation
Alberto Fabrizio, Ksenia R Briling, David D Girardier, et al.
Digital Discovery
|
May 11, 2026
Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states
Yuri Cho, Ksenia R Briling, Yannick Calvino Alonso, et al.
Digital Discovery
|
May 17, 2024
Benchmarking machine-readable vectors of chemical reactions on computed activation barriers
Puck van Gerwen, Ksenia R Briling, Yannick Calvino Alonso, et al.
Journal of Chemical Information and Modeling
|
July 15, 2024
3DReact: Geometric Deep Learning for Chemical Reactions
Puck van Gerwen, Ksenia R Briling, Charlotte Bunne, et al.
Faraday Discussions
|
December 18, 2024
Discovering synthesis targets: general discussion
Andy S Anker, Alán Aspuru-Guzik, Tim Bechtel, et al.
Page
of 2