Language selection

Government of Canada / Gouvernement du Canada

Search

Inverse design of a graphene-based quantum transducer via neuroevolution

From National Research Council Canada

Download
  1. (PDF, 14.1 MiB)
  2. (PDF, 465 KiB)
DOIResolve DOI: https://doi.org/10.1021/acs.jpcc.0c06903
AuthorSearch for: ORCID identifier: https://orcid.org/0000-0001-6933-3856; Search for: ORCID identifier: https://orcid.org/0000-0002-5846-1673; Search for: 1ORCID identifier: https://orcid.org/0000-0002-8146-6667
Affiliation
  1. National Research Council of Canada. Security and Disruptive Technologies
FormatText, Article
Subjectchemical structure; optimization; Hamiltonians; lattices; doping
Abstract
We introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very large configuration space of nanoelectronic devices. Our non-linear optimization procedure operates on trial Hamiltonians through superoperators controlling growth policies of regions of distinct doping. We demonstrate that our algorithm optimizes the doping of graphene-based three-terminal devices for valleytronics applications, monotonously converging to synthesizable devices with high merit functions in a few thousand evaluations (out of ≃2³⁸⁰⁰ possible configurations). The best-performing device allowed for a terminal-specific separation of valley currents with ≃96% (≃94%) K(K′) valley purity. Importantly, the devices found through our non-linear optimization procedure have both a higher merit function and higher robustness to defects than the ones obtained through geometry optimization.
Publication date
PublisherAmerican Chemical Society
In
  • The Journal of Physical Chemistry C 124, no. 48: 26117–26123.
LanguageEnglish
Peer reviewedYes
Export citationExport as RIS
Report a correctionReport a correction (opens in a new tab)
Record identifiera3425708-5b39-4cc4-a8f5-c01b3d2cd0d6
Record created2021-06-15
Record modified2021-09-21
Date modified: