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Found 3 papers, 1 papers with code
A data-driven interpretation of the stability of molecular crystals
Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem.
Improving Sample and Feature Selection with Principal Covariates Regression
Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it can be used to improve the computational performance, and also often the transferability, of a model.
Structure-Property Maps with Kernel Principal Covariates Regression
Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models.
