HDR Institute: Institute for Data Driven Dynamical Design

From molecules to robots, designing for dynamics has common theoretical underpinnings despite differences in length and time scale. However, such research is often overwhelmed by the high dimensional design space. The Institute for Data-Driven Dynamical Design addresses the challenge of prediction of dynamical processes in materials, including ion and molecular transport, catalytic pathways, and phase transformations in metamaterials, with a focus on discovering fundamentally new mechanisms and pathways.

This research represents a paradigm shift from traditional material efforts involving incremental improvements in ground-state and steady-state properties. Developments in the data sciences target (i) strategies for encoding complex structures and mechanistic pathways for machine intelligence, (ii) new predictive capabilities for evolving systems, and (iii) advances in visualization and integrating machine and human expertise.

Fueling these data science developments are large-scale simulations of dynamical processes across high dimensional design spaces. Experimental validation of these large-scale simulations addresses both end-product prediction and mechanistic pathways therein. The Institute's data science innovations may advance fields both within and beyond STEM involving complex time-evolving systems including molecular biology, atmospheric science, geophysics, and physical cosmology.

The Institute seeks to grow and unite the dispersed data-driven design community. Long-term growth is sought through outreach activities involving (i) high school coding schools, (ii) undergraduate involvement in data-rich research, and (iii) a post-baccalaureate bridge program that introduces students to data sciences and motivate them to pursue higher degrees.

Data-driven design community activities include (i) interdisciplinary summer schools and workshops, (ii) a Fellows program to collaboratively grow and disseminate the Institute’s developments, and (iii) dedicated efforts to create open-source software for the design community. Throughout these efforts, the Institute actively seeks to recruit, retain, and graduate a diverse array of students in STEM.

This virtual Institute seeks to design complex dynamical materials and structures through the union of machine and human intelligence. To learn dynamical behavior and ultimately discover new mechanisms, three core data science needs are addressed: (i) new representations and learning architectures that capture and encode the spatial arrangement, interactions, and temporal evolution of complex materials and geometrical structures, (ii) efficient exploration of high dimensional, time-dependent design spaces, and (iii) new visual analytics tools to quantitatively incorporate human-in-the-loop design feedback.

Advances in each of these areas form a virtuous cycle that accelerates discovery of new materials, driven by new mechanisms. This Institute converges an interdisciplinary team focused on four design spaces at their `tipping point', where large quantities of dynamical data can be readily created: (i) crystalline solids with tailored ion transport for fuel cells and batteries, (ii) pressure-sensitive metamaterials for robotics, (iii) light driven catalytic reactions for chemical production, and (iv) synthesis and assembly of porous frameworks for chemical separations.

These four areas are testbeds for cyberinfrastructure development for the broader scientific community. Interwoven throughout these activities are dedicated activities to build a new generation of STEM talent at the intersection of data science and the physical sciences/engineering and to broaden participation in STEM through targeted outreach.

This project is part of the National Science Foundation's Big Idea activities in Harnessing the Data Revolution (HDR). The award by the Office of Advanced Cyberinfrastructure is jointly supported by the Divisions of Chemistry, Materials Research, and Mathematical Sciences within the NSF Directorate for 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.