We have seen the use of Machine Learning (ML) increase significantly across multiple scientific domains over the past decade. Now we have seen increased investiture in the materials space. While public datasets that support pharmaceutical research are widespread this is not the case for materials. More often than not we have seen Materials companies focus their ML exploration into specific needs that underpin their business model where little public data is available. This is due to a highly faceted nature of the materials industry where data is not readily transferable between areas. Here we explore some of the insights and learnings that CAS has discovered in development of custom datasets for materials ML.<br/>

CINFull materials: Insights on development of custom feature set data for machine learning in materials

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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