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- W3110326884 abstract "In this variance-motivated study, the variance of a multi-thousand molecular dataset of Coulomb matrices is analyzed. This paper presents novel statistical methods and models that can aide in molecular prediction and analysis. A blended statistical/ML model is introduced for classifying data as Normal as well as a model for visualizing variance. Linear regression is also used to show a potential simple and 1 dimensional molecular descriptor, for some molecules." @default.
- W3110326884 created "2020-12-07" @default.
- W3110326884 creator A5016886689 @default.
- W3110326884 date "2020-11-19" @default.
- W3110326884 modified "2023-09-23" @default.
- W3110326884 title "Modeling Variance: A Variance-Motivated Approach to Molecular Prediction" @default.
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- W3110326884 doi "https://doi.org/10.26434/chemrxiv.13239467.v2" @default.
- W3110326884 hasPublicationYear "2020" @default.
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