Award Abstract # 2236190
NSF Convergence Accelerator Track I: Mind over Matter: Socioresilient Materials Design: A New Paradigm For Addressing Global Challenges in Sustainability

NSF Org: ITE
Innovation and Technology Ecosystems
Recipient: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Initial Amendment Date: December 14, 2022
Latest Amendment Date: July 25, 2023
Award Number: 2236190
Award Instrument: Standard Grant
Program Manager: Linda Molnar
ITE
 Innovation and Technology Ecosystems
TIP
 Directorate for Technology, Innovation, and Partnerships
Start Date: December 15, 2022
End Date: June 30, 2024 (Estimated)
Total Intended Award Amount: $750,000.00
Total Awarded Amount to Date: $750,000.00
Funds Obligated to Date: FY 2023 = $750,000.00
History of Investigator:
  • Christine Ortiz (Principal Investigator)
    cortiz@mit.edu
  • James Saal (Co-Principal Investigator)
  • Jingjie Yeo (Co-Principal Investigator)
  • Francisco Martin-Martinez (Co-Principal Investigator)
Recipient Sponsored Research Office: Massachusetts Institute of Technology
77 MASSACHUSETTS AVE
CAMBRIDGE
MA  US  02139-4301
(617)253-1000
Sponsor Congressional District: 07
Primary Place of Performance: Massachusetts Institute of Technology
77 MASSACHUSETTS AVE
CAMBRIDGE
MA  US  02139-4301
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): E2NYLCDML6V1
Parent UEI: E2NYLCDML6V1
NSF Program(s): Convergence Accelerator Resrch
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 131Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.084

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Zhai, Hanfeng and Hao, Hongxia and Yeo, Jingjie "Benchmarking inverse optimization algorithms for materials design" APL Materials , v.12 , 2024 https://doi.org/10.1063/5.0177266 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

A critical challenge in fostering materials sustainability is the lack of standardized tools, data, metrics, and information for the holistic and comprehensive evaluation of a sizable number of materials sustainability-relevant factors across the materials value chain. To address this challenge, this transformative cross-disciplinary and cross-sector venture is developing the first socioresilient DECISION-support software system which integrates and optimizes hundreds of sustainability-related materials technical (T), environmental (E), social (S), and financial (F) metrics, as actionable intelligence for accelerating materials research and development (R&D) sustainability and advancing materials value chain sustainability. Empowering a user base of R&D professionals, DECISION accelerates cost-effective sustainable new product innovation and development, advances market alignment with materials sustainability, mitigates product risks associated with lack of incorporation of sustainability factors, and aligns with dynamic regulatory requirements.  The DECISION software platform is initially developing end user-informed product software modules utilizing state-of-the-art computational methods to build the platform including multi-objective optimization, machine learning (ML) and predictive analytics, and deep search, big data analytics, and high-throughput calculations. A beachhead market and testbed prototype for polyvinyl chloride was selected due to its widespread use and large market size, potential for sustainability innovation and advancement, availability of data, and alignment with team expertise. Through the creation of open source, Materials Data as a Service (MDaS), and Materials Computing as a Service (MCaS) data and software this project holds great potential to accelerate and converge the materials sustainability R&D ecosystem and act as a force multiplier for materials sustainability R&D, resulting in broad reaching planetary and societal impact.


Last Modified: 10/29/2024
Modified by: Christine Ortiz

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