Machine learning is rapidly changing our society, with computers recently gaining skills in many new tasks. These tasks range from understanding language to driving cars. Materials science and engineering is also being transformed. Many tasks are becoming increasingly accessible to machine learning algorithms. These range from predicting new data to analyzing images. Many basic machine learning algorithms are readily available. However the overall workflow involved in the application of machine learning for materials problems is still largely executed by hand. Getting results out is still done by traditional methods like publishing articles. There is an enormous opportunity to accelerate the growth and impact of machine learning in materials research. This requires improved cyberinfrastructure. This project will develop an approach to accelerate the entire machine learning workflow. Its output will include tools to easily develop datasets, manage model development, and output models. These will be reusable and reproducible for future use. This project will enable materials scientists and engineers to rapidly develop and deploy machine learning models. More importantly, the entire materials community will be able to quickly access these models. It will transform how we discover and develop advanced materials.

The project will have three major technical components: (i) A MAterials Simulation Toolkit for Machine Learning (MAST-ML) with workflow tools that will enable local or cloud-based multistep, automated execution of complex machine learning data analysis and model training, codified best practices, increased access to machine learning methods for non-experts, and accelerated model development; (ii) The Foundry Materials Informatics Environment that will provide flexible, integrated, cloud-based management of machine learning materials science and engineering projects, from organizing data to developing models to disseminating results that are machine and human accessible and reproducible in ways that support a networked materials innovation ecosystem, (iii) Representative science applications of machine learning materials science and engineering projects that will support infrastructure development and promotion, as well as demonstrate best practices on state-of-the-art materials science and engineering problems. In addition to its impact on materials science and engineering, this project will develop students and young researchers with the interdisciplinary skills of machine learning and materials science and engineering, and promote these new ideas to the broader materials community. This award is jointly supported by the NSF Office of Advanced Cyberinfrastructure, and the Division of Materials Research within the NSF Directorate of Mathematical and Physical Sciences.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Advanced CyberInfrastructure (ACI)
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Seung-Jong Park
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University of Wisconsin Madison
United States
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