Designer 3D Mesoscale Materials Synthesized in the Self-Assembly Foundry

Project Personnel

Alexander Katz

Principal Investigator

Massachusetts Institute of Technology

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Vincenzo Vitelli

University of Chicago

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Caroline Ross

Massachusetts Institute of Technology

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Funding Divisions

Division of Materials Research (DMR)

Self-assembly is one of the most promising avenues for the manufacturing/synthesis of materials and systems with exquisite control over nanoscale features while being fast, scalable, and inexpensive. It could enable the next revolution in integrated systems and designer mesoscale materials for multiple applications including information systems, sensing, actuation, and artificial intelligence. However, there are still many challenges in utilizing self-assembly as a precision fabrication technique. 

This project will develop a self-assembly foundry by implementing a dual assembly line in which experimental, molecular simulations, and artificial intelligence techniques are deployed simultaneously to create designer 3-dimensional (3D) nanostructured systems. This effort will examine the limits of 3D self-assembly to accelerate the fabrication of custom systems, from interconnected nanosystems used in computer chips to designer mechanical nanostructures for sensing and actuation. Particular emphasis will be placed in developing new manufacturing routes using topological principles. 

The broader impacts of this project envision a basis for training a new generation of scientist or engineers that can engage effectively with industry and academia. This project will also lead to the training of community college students and the development of online learning materials, as well as public engagement activities. The convergence of academia and industry; theory, computation, and experiment; different mentoring perspectives; and the high-level view of the self-assembly manufacturing process will provide a rich environment for the participants to develop new knowledge, skills, and abilities, with a strong emphasis on training and knowledge transfer.

Publications

Machine learning interpretable models of cell mechanics from protein images
M. S. Schmitt, J. Colen, S. Sala, J. Devany, S. Seetharaman, A. Caillier, M. L. Gardel, P. W. Oakes, and V. Vitelli
1/1/2024
Systematic generation of Hamiltonian families with dualities
M. Fruchart, C. Yao, and V. Vitelli
5/15/2023
Odd Viscosity and Odd Elasticity
M. Fruchart, C. Scheibner, and V. Vitelli
3/10/2023
Emergence of layered nanoscale mesh networks through intrinsic molecular confinement self-assembly
Z. Sun, R. Liu, T. Su, H. Huang, K. Kawamoto, R. Liang, B. Liu, M. Zhong, A. Alexander-Katz, C. A. Ross, and J. A. Johnson
1/9/2023
Experimental and Computational Evaluation of Self-Assembled Morphologies in Diblock Janus Bottlebrush Copolymers
R. Liu, Z. Sun, H. Huang, J. A. Johnson, A. Alexander-Katz, and C. A. Ross
12/22/2022
Observation of topological action potentials in engineered tissues
H. Ori, M. Duque, R. Frank Hayward, C. Scheibner, H. Tian, G. Ortiz, V. Vitelli, and A. E. Cohen
12/22/2022

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Designing Materials to Revolutionize and Engineer our Future (DMREF)