Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing-Reference-Cited by-同舟云学术

Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing

Published:2020-09-08 Issue:10 Volume:14 Page:13406-13417
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Frey Nathan C.¹^ORCID,Akinwande Deji²,Jariwala Deep³^ORCID,Shenoy Vivek B.¹

Affiliation:

1. Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States

2. Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States

3. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States

Funder

Army Research Office

Division of Electrical, Communications and Cyber Systems

Division of Civil, Mechanical and Manufacturing Innovation

National Science Foundation

Division of Materials Research

Publisher

American Chemical Society (ACS)

Subject

General Physics and Astronomy,General Engineering,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.0c05267

Reference71 articles.

1. Photonics and optoelectronics of 2D semiconductor transition metal dichalcogenides

2. Quantum emission from hexagonal boron nitride monolayers

3. Zero-static power radio-frequency switches based on MoS2 atomristors

4. Atomristor: Nonvolatile Resistance Switching in Atomic Sheets of Transition Metal Dichalcogenides

5. Thinnest Nonvolatile Memory Based on Monolayer h‐BN

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