Autonomous Computing Materials
Name
acsnano.0c09556.pdf
Description
Published version
Size
1.74 MB
Format
Adobe PDF
Checksum (MD5)
9ffe83854c44c406acc3ec04bea33dc7
Author(s)
Bathe, Mark
Hernandez, Rigoberto
Komiyama, Takaki
Machiraju, Raghu
Neogi, Sanghamitra
Date Issued
2021
Journal
ACS Nano
Publisher
American Chemical Society (ACS)
Version
Final published version
Abstract
Conventional materials are reaching their limits in computation, sensing, and data storage capabilities, ushered in by the end of Moore's law, myriad sensing applications, and the continuing exponential rise in worldwide data storage demand. Conventional materials are also limited by the controlled environments in which they must operate, their high energy consumption, and their limited capacity to perform simultaneous, integrated sensing, computation, and data storage and retrieval. In contrast, the human brain is capable of multimodal sensing, complex computation, and both short- and long-term data storage simultaneously, with near instantaneous rate of recall, seamless integration, and minimal energy consumption. Motivated by the brain and the need for revolutionary new computing materials, we recently proposed the data-driven materials discovery framework, autonomous computing materials. This framework aims to mimic the brain's capabilities for integrated sensing, computation, and data storage by programming excitonic, phononic, photonic, and dynamic structural nanoscale materials, without attempting to mimic the unknown implementational details of the brain. If realized, such materials would offer transformative opportunities for distributed, multimodal sensing, computation, and data storage in an integrated manner in biological and other nonconventional environments, including interfacing with biological sensors and computers such as the brain itself.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/ACSNANO.0C09556
