3 papers
physics.chem-ph2025
Integer linear programming for unsupervised training set selection in molecular machine learning
Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza +4
Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-i…
physics.chem-ph2024
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-ph2024
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…