NSF Convergence Accelerator Track I: Mind over Matter: Socioresilient Materials Design: A New Paradigm For Addressing Global Challenges in Sustainability

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

This project, NSF Convergence Accelerator Track I: Mind over Matter: Socioresilient Materials Design (SMD): A New Paradigm For Addressing Global Challenges in Sustainability (MoMaTS), will be an innovative, convergent cross-sector and cross-disciplinary effort to fundamentally re-think, re-shape, re-direct, and accelerate emergent technical capabilities in materials research and development towards more environmentally, socially, and economically sustainable materials-based products and materials-driven outcomes. Increasingly, our materials infrastructure and systems are faced with climate shocks which amplify environmental and social injustice issues over long periods of time. The current state of circular materials design approaches are insufficient to address challenges, and even have potential to simultaneously degrade resiliency, as well as to create downstream unintended negative societal impacts. This project brings together a team from industry (Citrine), academia (MIT, Cornell, Swansea), and the social sector (Station1) to focus on this issue via the intentional design of materials which foster the equitable capacity of human communities to cope with, adapt to, and recover from stresses and shocks, through consideration of the often bridging technical, environmental, and social systems. This project is critical for, and has great potential to, advance environmental protection and resource conservation, social well-being and equity, economic prosperity and continuity, infrastructure resiliency and national security. This project will develop the new field of “Socioresilient Materials Design” (SMD) by building upon the classic materials design paradigm (structure - property - processing - performance relationships) through a core convergence approach - integrating circular design principles, powerful emergent materials computational capabilities (i.e. multiscale computational materials design based on physicochemical laws such as molecular dynamics and density functional theory, artificial intelligence (AI) / machine learning (ML), evolutionary optimization algorithms), and rigorous humanistic and social sciences methodologies to understanding and fostering socioresilient societal impacts. This project will develop a SMD framework, inventory of metrics, knowledge base of novel design approaches, advanced computational methods, exemplar use case studies, datasets, and an open software tool, for utilization in decision-making processes. SMD parameters, metrics, and constraints, in addition to traditional material properties, will be incorporated into advanced computational materials design workflows for multi-objective optimization and to quantify and understand the inherent trade-offs present. Methodologies and software tools will be developed to visualize and assess such trade-offs between technical and SMD metrics in multi-parametric design spaces. The codification and dissemination of project research results will have a broad reaching impact across disparate disciplines including open software, publications for scholarly audiences, contributions to pedagogy and curriculum, influence on emergent research in the academic and start-up communities, the creation of new collaborations, and translation of research outcomes into opportunities for public engagement. This research will serve as a basis for undergraduate curriculum development and delivery and include broadening participation through research projects for STEM undergraduate students from historically under-represented backgrounds with an emphasis on participation by students enrolled in under-resourced higher education institutions across the United States. 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.