The overarching goal of this project is to accelerate the discovery of materials with tailored electronic properties through human-computer active search. These efforts will lay the groundwork for accelerating materials discovery, and advance the capability to control electronic properties in materials with the potential for profound societal impact. The thermoelectric and photocatalytic materials predicted, synthesized, and characterized in this research can realize societal advances in the space of energy and solar fuels. High-efficiency thermoelectric materials can revolutionize how heat sources are transformed into electrical power by eliminating the traditional intermediate mechanical energy conversions. Earth-abundant light-responsive catalysts are emerging as an alternative to costly, rare metal catalysts to store solar energy as portable liquid fuels, like ethanol. These green reactions are enabling low-cost, carbon-neutral fuels. The team brings together expertise in materials science, chemistry, machine learning, visualization, metadata, and knowledge frameworks to develop multi-fidelity, expert-guided active search strategies within materials science and chemistry. Resonances among the team's existing outreach programs will broaden inclusion of students from underrepresented groups and be moderated via the Alliance for Diversity in Science and Engineering. The work will provide cross-disciplinary training to graduate students and postdocs in all aspects of material informatics, including participating in and leading team efforts, co-mentorship of Ph.D. and postdoctoral researchers, inclusive symposia at national conferences, and a summer workshop focused on the intersection of visualization, machine learning, ontological engineering and materials science. Through enabling the acceleration of the discovery of new materials, this project supports the goals of the Materials Genome Initiative.
An interdisciplinary team will create a search framework for scientific discovery that leverages recent advances in material databases, machine learning, visualization, human-machine interaction, and knowledge structures. To broadly assess the efficacy of this approach, the search effort will span the electronic behavior of both molecules and crystalline materials: (i) new organic photocatalysts for solar fuels production and (ii) new thermoelectric materials for electricity generation. Central to this effort is the engagement of domain experts and associated feedback in a human-in-the-loop active search process. Dynamic visualizations will enable the user to (i) understand the underlying reasons why the materials are being suggested and (ii) provide a user steering capability to identify and annotate specific aspects of the explored search space. Domain-expert annotations and feedback will be parsed against a suite of ontologies, further aiding the search process by providing relational insight between features. New molecules and materials will be explored through a combination of first principles calculations and high-throughput, automated experimentation; these results will be incorporated into a continually growing open-access database. Efficiently integrating and directing evolving data-streams from experiment, computation, and human steering during the search will be achieved with a multi-fidelity active search policy. Through enabling the acceleration of the discovery of new materials, this project supports the goals of the Materials Genome Initiative.
This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR 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.
