Machine learning descriptors in materials chemistry used in multiple experimentally validated studies: Oliynyk elemental property dataset.

ABSTRACT: Materials informatics employs data-driven approaches for analysis and discovery of materials. Features also referred to as descriptors are essential in generating reliable and accurate machine-learning models. While general data can be obtained through public and commercial sources, features must be tailored to specific applications. Common featurizers suitable for generic chemical problems may not be effective in features-property mapping in solid-state materials with ML models. Here, we have assembled the Oliynyk property list for compositional feature generation, which performs well on limited datasets (50 to 1000 training data points) in the solid-state materials domain. The dataset contains 98 elemental features for atomic numbers from 1 to 92, including thermodynamic properties, elec

SUBMITTER: Lee S 

PROVIDER: S-EPMC10879764 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature