The Materials Genome Initiative is transforming Materials Science into a data-rich discipline. These developments open exciting opportunities for knowledge discovery in materials databases using informatics approaches to inform the rational design of novel materials with the desired physical and chemical properties. Statistical and data mining approaches have been successfully employed in both chemistry and biology leading to the development of cheminformatics and bioinformatics, respectively. However, until recently their application in materials science has been limited due to the lack of a sufficient body of data, methods, and reliable infrastructure.<br/><br/>A need for such methods is driven by communities of materials scientists with often unique and heterogeneous requirements. This data infrastructure should allow online access to materials data to provide information quickly and easily. It should be available to warehouse, search, and curate materials data generated by both experiments and calculations. Community-developed standards should provide the format, metadata, data types, and protocols necessary for seamless data transfer. This effort should include methods for capturing data, incorporating these methods into existing workflows, and developing and sharing workflows.<br/><br/>In this work we showcase a pilot materials informatics platform capable of (i) instantaneously query and retrieve the necessary material information in the form of web app and RESTful API; (ii) identify, visualize and study important data patterns, and (iii) generate experimentally-testable hypotheses by building predictive Machine Learning (ML) models based on materials fingerprints and descriptors. Our computational approach relies on ML methodologies that our group has developed and employed successfully to enable a rational design of organic compounds with desired properties.

Machine learning (ML) accelerated materials discovery

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.