Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session L48: Superconductivity: Theories and Models
8:00 AM–11:00 AM,
Wednesday, March 4, 2020
Room: Mile High Ballroom 1A
Sponsoring
Unit:
DCMP
Chair: Peter Hirschfeld, University of Florida
Abstract: L48.00007 : Using unsupervised machine learning to predict critical temperatures of superconductors
Presenter:
Sasa Dordevic
(Univ of Akron)
Authors:
Benjamin Roter
(Univ of Akron)
Sasa Dordevic
(Univ of Akron)
construct element vectors and then perform unsupervised learning
of critical temperatures. Only the chemical composition of
superconductors is used in this procedure. No physical predictors
(neither experimental nor numerical) of any kind are used. We
archive R2=0.93 which is comparable and in some cases higher
then similar estimates using other artificial intelligence
techniques. Based on this machine learning model, we predict
several new superconductors with high critical temperatures. We
also discuss the factors that impede the learning process and
suggest possible ways to fix them.
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