Title:
How Materials Can Learn to Function

dc.contributor.author Liu, Andrea J.
dc.contributor.corporatename Georgia Institute of Technology. School of Physics en_US
dc.contributor.corporatename University of Pennsylvania. Dept. of Physics and Astronomy en_US
dc.date.accessioned 2021-04-08T19:19:45Z
dc.date.available 2021-04-08T19:19:45Z
dc.date.issued 2021-03-29
dc.description Presented online on March 29, 2021 at 3:00 p.m. en_US
dc.description Andrea J. Liu is the Hepburn Professor of Physics at the University of Pennsylvania, where she holds a joint appointment in the Department of Chemistry. She is a theoretical physicist studying condensed matter physics and biophysics.
dc.description Runtime: 69:31 minutes
dc.description.abstract How does learning occur? In the context of neural networks, learning occurs via optimization, where a loss function is minimized to achieve the desired result. But physical networks such as mechanical spring networks or flow networks cannot minimize such a loss function by themselves—they need the help of a computer. An alternative is to encode local rules into those networks so that they can evolve under external driving to develop function. For example, if the springs in a mechanical network have equilibrium lengths that grow if the springs are stretched, and shrink when the springs are compressed, the network will naturally evolve under applied stresses. I will describe how both of these strategies—global minimization of a loss function as well as training by local rules--can be used to teach materials how to perform functions inspired by biology, such as the ability of proteins (e.g. hemoglobin) to change their conformations upon binding of an atom (oxygen) or molecule, or the ability of the brain’s vascular network to send enhanced blood flow and oxygen to specific areas of the brain associated with a given task. en_US
dc.format.extent 69:31 minutes
dc.identifier.uri http://hdl.handle.net/1853/64429
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries Physics Colloquium
dc.subject Allostery en_US
dc.subject Learning en_US
dc.subject Negative Poisson ratio en_US
dc.title How Materials Can Learn to Function en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Physics
local.relation.ispartofseries Physics Colloquium
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication 2ba39017-11f1-40f4-9bc5-66f17b8f1539
relation.isSeriesOfPublication 5fcf4984-0912-45ae-91c5-2c6de98772b0
Files
Original bundle
Now showing 1 - 4 of 4
No Thumbnail Available
Name:
aliu.mp4
Size:
297.5 MB
Format:
MP4 Video file
Description:
Download Video
No Thumbnail Available
Name:
aliu_videostream.html
Size:
1.32 KB
Format:
Hypertext Markup Language
Description:
Streaming Video
No Thumbnail Available
Name:
transcript.txt
Size:
53.42 KB
Format:
Plain Text
Description:
Transcription
Thumbnail Image
Name:
thumbnail.jpg
Size:
80.79 KB
Format:
Joint Photographic Experts Group/JPEG File Interchange Format (JFIF)
Description:
Thumbnail
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.13 KB
Format:
Item-specific license agreed upon to submission
Description:
Collections