Search Results for author: Clemence Corminboeuf

Found 2 papers, 1 papers with code

EquiReact: An equivariant neural network for chemical reactions

no code implementations13 Dec 2023 Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Ruben Laplaza, Andreas Krause, Clemence Corminboeuf

Equivariant neural networks have considerably improved the accuracy and data-efficiency of predictions of molecular properties.

Property Prediction

Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation

1 code implementation14 Oct 2020 Alberto Fabrizio, Ksenia R. Briling, David D. Girardier, Clemence Corminboeuf

The on-top pair density [$\Pi(\vec{r})$] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space.

Chemical Physics Quantum Physics

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