Abstract Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, such as those of crystalline compounds, a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights. In this work, we introduce an algebraic topology-based method, called atom-specific persistent homology (ASPH), as a unique representation of crystal structures. The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at variousmore »
This content will become publicly available on February 7, 2023
DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions
We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR) (https://www.github.com/sparks-baird/mat_discover), a Python tool for identifying and assessing high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model. In this work, we create pairwise distance matrices between compounds via Element Mover's Distance (ElMD) and use these to create 2D density-aware embeddings for chemical compositions via Density-preserving Uniform Manifold Approximation and Projection (DensMAP). Because ElMD assigns distances between compounds that are more chemically intuitive than Euclidean-based distances, the compounds can then be clustered into chemically homogeneous clusters via Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN*). In combination with performance predictions via Compositionally-Restricted Attention-Based Network (CrabNet), 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 multi-objective Pareto front plots and assign a weighted score to each composition that encompasses the trade-off between performance and density-based chemical uniqueness. In addition to density-based metrics, we explore an additional uniqueness proxy related to property gradients in DensMAP space. As a validation study, we use DiSCoVeR to screen materials for both performance and uniqueness more »
- Award ID(s):
- 1950589
- Publication Date:
- NSF-PAR ID:
- 10319410
- Journal Name:
- Digital Discovery
- ISSN:
- 2635-098X
- Sponsoring Org:
- National Science Foundation
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