Organically-templated metal oxide framework compounds have outstanding structural and chemical diversity, which lends them to applications for industrial catalysis, gas separation, and optical engineering. Yet, despite several decades of experimental effort, making new examples of these materials is a time-consuming trial-and-error process. Most of the chemical reactions that have been performed are deemed "unsuccessful" because they do not result in a crystalline product, and are never reported in the literature. There is no forum for collecting these experiments, nor a means for deriving value from them. Nevertheless, these "dark reactions" are valuable because they define the bounds on the reaction conditions needed to successfully produce a product. By providing a searchable online repository for reaction data, we will enable better management and sharing of these dark reactions. Moreover, we will use this data as a resource to train machine learning (aka statistical learning or data-mining) algorithms that predict the success of reactions ahead of time. Based on the machine learning predictions, we will perform experimental validation to test the predictions of the model.
Our project will provide a mechanism for collecting the dark reactions and then using them to guide future reactions to be more successful, reducing the researcher time and cost of reagents needed to synthesize new materials. This will accelerate and lower the cost (in researcher time and materials) of discovering new materials. This directly addresses the call of the White House Office of Science and Technology Policy's 2011 Materials Genome Initiative, specifically finding ways to use computation to bring functional materials to market more quickly. Second, this project will serve as a model for collaboration between chemists and computer scientists that can be directly transferred to a wide range of other disciplines and avenues of investigation. Third, we will provide a cohesive, comprehensive, interdisciplinary and sustained research experience for undergraduate students, thus contributing to the scientific workforce. Fourth, our outreach activities will foster interest in data-driven techniques, create a network of collaborating laboratories and provide the software infrastructure to others wishing to initiate related projects.