Materials Science

DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions



We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.


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Supplementary weblinks

DiSCoVeR Codebase
A materials discovery algorithm geared towards exploring high performance candidates in new chemical spaces.
Trained Materials Discovery Python Class
Load via: ```python import pickle with open("disc.pkl", "rb") as f: disc = pickle.load(f) ``` To see the attributes of the Discover() class: ```python dir(disc) ```