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• W2895974039 abstract "In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation. This enables us to study the relative importance of each interaction block for property prediction. We demonstrate on both the QM9 and MD17 dataset that their relative weighting depends strongly on the chemical composition and configurational degrees of freedom of the molecules which opens the path towards a more detailed understanding of machine learning models for molecules." @default.
• W2895974039 created "2018-10-26" @default.
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• W2895974039 date "2018-10-23" @default.
• W2895974039 modified "2023-09-27" @default.
• W2895974039 title "Analysis of Atomistic Representations Using Weighted Skip-Connections" @default.
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