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physics.chem-ph2023★ 22 cited
3DReact: Geometric deep learning for chemical reactions
Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne +4
Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficie…
physics.chem-ph2023★ 10 cited
SPAM(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations
Ksenia R. Briling, Yannick Calvino Alonso, Alberto Fabrizio +1
Recently, we introduced a class of molecular representations for kernel-based regression methods -- the spectrum of approximated Hamiltonian matrices (SPAM) -- that ta…