273 citations · 475 across the 11 of their papers we have counts for
11 papers
Range separation of the interaction potential in intermolecular and intramolecular symmetry-adapted perturbation theory
Du Luu, Clemence Corminboeuf, Konrad Patkowski
Symmetry-adapted perturbation theory (SAPT) is a popular and versatile tool to compute and decompose noncovalent interaction energies between molecules. The intramolecular SAPT (IS…
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…
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…
Assessing the persistence of chalcogen bonds in solution with neural network potentials
Veronika Juraskova, Frederic Celerse, Ruben Laplaza +1
Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry and functional materials to name a few. Yet, their co…
SPAM: the Spectrum of Approximated Hamiltonian Matrices representations
Alberto Fabrizio, Ksenia R. Briling, Clemence Corminboeuf
Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity,…
Impact of quantum-chemical metrics on the machine learning prediction of electron density
Ksenia R. Briling, Alberto Fabrizio, Clemence Corminboeuf
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain th…
Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation
Alberto Fabrizio, Ksenia R. Briling, David D. Girardier +1
The on-top pair density [] is a local quantum-chemical property that reflects the probability of two electrons of any spin to occupy the same position in sp…
Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CHSOH and HO in phenol
Kevin Rossi, Veronika Juraskova, Raphael Wischert +3
We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent u…
Learning the energy curvature versus particle number in approximate density functionals
Alberto Fabrizio, Benjamin Meyer, Clemence Corminboeuf
The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of d…
Getting the Right Twist: Influence of Donor-Acceptor Dihedral Angle on Exciton Kinetics and Singlet-Triplet Gap in Deep Blue Thermally Activated Delayed Fluorescence Emitter
Sebastian Weissenseel, Nikita A. Drigo, Liudmila G. Kudriashova +9
Here, a novel deep blue emitter SBABz4 for use in organic light-emitting diodes (OLED) is investigated. The molecular design of the emitter enables thermally activated delayed fluo…
A Transferable Machine-Learning Model of the Electron Density
Andrea Grisafi, David M. Wilkins, Benjamin A. R. Meyer +3
The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structu…