One class classification as a practical approach for accelerating π-π co-crystal discovery.

ABSTRACT: The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound poly

SUBMITTER: Vriza A 

PROVIDER: S-EPMC8179233 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature