Topological representations of crystalline compounds for the machine-learning prediction of materials properties.

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 various scales. Combined with composition-based attributes, ASPH-based ML model provides a highly acc

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC8528346 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature