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
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.
- mat_discover API
- GitHub Source