Welcome to Chemically Novel Materials Discovery!

This is the documentation for mat_discover. Descending from Stochastic Clustering Variance Regression (DiSCoVeR) is a materials discovery algorithm for composition- or structure-based materials. DiSCoVeR emphasizes exploring high-performance candidates in new chemical spaces as originally proposed by Baird, Diep, and Sparks (Digital Discovery, ChemRxiv). The library mat_discover provides a GPU-accelerated (and CPU-compatible) Python implementation and tools for analyzing the outputs. mat_discover will “suggest your next experiment” using a custom training dataset of chemical formula or structure and target properties and a custom validation set of potential candidates. All in just a few lines of code! Get started learning about DiSCoVeR.

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