RII Track-4: NSF: Obtaining Data Science Expertise to Enable Rapid Data Driven Material Discovery

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Designing new materials for advanced applications, despite being costly, are important for improving the US economy and national security. Chemical tunability provides almost infinite possibilities to explore and discover new materials. In the meantime, it is almost impossible to sample all the available chemistry combinations for new materials due to limited time, labor, and resources. This greatly limits the speed of new materials discovery due to the above constraints in a typical academic research laboratory. Such limitation can be potentially addressed by recent developments in data science. Advanced artificial intelligence technologies have also been used to enable autonomous vehicles and humanoid robotics, and new drug discovery. Despite their large success in the industry, they have not been widely adopted in physical and materials sciences in academia. The new generation of computational science, supported by open-source platforms and databases, is likely to revolutionize the discovery of the next generation of advanced materials. Inspired by this backdrop, researchers at the University of Southern Mississippi see that data science will soon become an integral part of the scientific research skills of their students. Thus, this NSF EPSCoR RII Track-4 fellowship project provides a unique opportunity for them to acquire this emerging skill set to serve their research group, and broadly researchers in Mississippi through collaborative projects. Support from this project will also be uses to recruit and advance students traditionally represented at the University of Southern Mississippi.This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project would provide a fellowship to an Assistant professor at University of Southern Mississippi (USM) and support for a USM graduate student. This project supports a six-month fellowship visit to the world-class scientific computation facility at the Lawrence Berkeley National Laboratory to acquire the data-driven material discovery expertise for the research team from USM. The researchers from Mississippi will work with world-leading experts from the Center for Advanced Mathematics for Energy Research Applications (CAMERA) facility to receive hands-on training on high-throughput data collection and data science using microscopy and scattering tools to rapidly screen and synthesize new materials to recycle plastic wastes. The proposed data science skill could only be acquired through an extended on-site visit due to a high initial learning curve for newcomers, which can be uniquely enabled by this NSF EPSCoR RII Track-4 program. Using this new skill, this Mississippi research team will be able to rapidly synthesize and screen non-covalently bonded copolymer compatibilizers to better recycle the plastic wastes using plastic wastes collected in Mississippi and along the Gulf coast. This proposed data-driven material development skill would uniquely benefit the principal investigator throughout his career beyond this project time as a new methodology to tackle other scientific problems within his group. The fellowship could also provide unique research opportunities for resource-limited Mississippi STEM students. In addition, a new data science curriculum would be introduced for the first time at the USM. The project will help to address a diverse range of research challenges, not only inside USM but also in other institutions in Mississippi.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.