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Filippo Bigi

Showing results (1-10 of 12) with videos related to

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Physical Review Letters|June 26, 2026
Learning the Action for Long-Time-Step Simulations of Molecular DynamicsFilippo Bigi, Johannes Spies, Michele Ceriotti
The Journal of Chemical Physics|July 26, 2024
Wigner kernels: Body-ordered equivariant machine learning without a basisFilippo Bigi, Sergey N Pozdnyakov, Michele Ceriotti
The Journal of Chemical Physics|August 8, 2023
Fast evaluation of spherical harmonics with sphericartFilippo Bigi, Guillaume Fraux, Nicholas J Browning, et al.
The Journal of Chemical Physics|December 22, 2022
A smooth basis for atomistic machine learningFilippo Bigi, Kevin K Huguenin-Dumittan, Michele Ceriotti, et al.
Faraday Discussions|September 25, 2024
Prediction rigidities for data-driven chemistrySanggyu Chong, Filippo Bigi, Federico Grasselli, et al.
The Journal of Chemical Physics|February 13, 2026
Resolving the body-order paradox of machine learning interatomic potentialsSanggyu Chong, Tong Jiang, Michelangelo Domina, et al.
Digital Discovery|March 12, 2026
A universal machine learning model for the electronic density of statesWei Bin How, Pol Febrer, Sanggyu Chong, et al.
Nature Communications|November 27, 2025
PET-MAD as a lightweight universal interatomic potential for advanced materials modelingArslan Mazitov, Filippo Bigi, Matthias Kellner, et al.
The Journal of Chemical Physics|February 11, 2026
metatensor and metatomic: Foundational libraries for interoperable atomistic machine learningFilippo Bigi, Joseph W Abbott, Philip Loche, et al.
Faraday Discussions|December 18, 2024
Discovering synthesis targets: general discussionAndy S Anker, Alán Aspuru-Guzik, Tim Bechtel, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Physical Review Letters|June 26, 2026
Learning the Action for Long-Time-Step Simulations of Molecular DynamicsFilippo Bigi, Johannes Spies, Michele Ceriotti
The Journal of Chemical Physics|July 26, 2024
Wigner kernels: Body-ordered equivariant machine learning without a basisFilippo Bigi, Sergey N Pozdnyakov, Michele Ceriotti
The Journal of Chemical Physics|August 8, 2023
Fast evaluation of spherical harmonics with sphericartFilippo Bigi, Guillaume Fraux, Nicholas J Browning, et al.
The Journal of Chemical Physics|December 22, 2022
A smooth basis for atomistic machine learningFilippo Bigi, Kevin K Huguenin-Dumittan, Michele Ceriotti, et al.
Faraday Discussions|September 25, 2024
Prediction rigidities for data-driven chemistrySanggyu Chong, Filippo Bigi, Federico Grasselli, et al.
The Journal of Chemical Physics|February 13, 2026
Resolving the body-order paradox of machine learning interatomic potentialsSanggyu Chong, Tong Jiang, Michelangelo Domina, et al.
Digital Discovery|March 12, 2026
A universal machine learning model for the electronic density of statesWei Bin How, Pol Febrer, Sanggyu Chong, et al.
Nature Communications|November 27, 2025
PET-MAD as a lightweight universal interatomic potential for advanced materials modelingArslan Mazitov, Filippo Bigi, Matthias Kellner, et al.
The Journal of Chemical Physics|February 11, 2026
metatensor and metatomic: Foundational libraries for interoperable atomistic machine learningFilippo Bigi, Joseph W Abbott, Philip Loche, et al.
Faraday Discussions|December 18, 2024
Discovering synthesis targets: general discussionAndy S Anker, Alán Aspuru-Guzik, Tim Bechtel, et al.
Pageof 2