Benchmark datasets incorporating diverse tasks, sample sizes, material systems, and data heterogeneity for materials informatics.

ABSTRACT: Materials discovery via machine learning has become an increasingly popular method due to its ability to rapidly predict materials properties in a time-efficient and low-cost manner. However, one limitation in this field is the lack of benchmark datasets, particularly those that encompass the size, tasks, material systems, and data modalities present in the materials informatics literature. This makes it difficult to identify optimal machine learning model choices including algorithm, model architecture, data splitting, and data featurization for a given task. Here, we attempt to address this lack of benchmark datasets by assembling a unique repository of 50 different datasets for materials properties. The data contains both experimental and computational data, data suited for regression a

SUBMITTER: Henderson AN 

PROVIDER: S-EPMC8319566 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature