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Title: Modeling Electronic Quantum Transport with Machine Learning

Journal Article · · Physical Review
 [1];  [2]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Basel (Switzerland)
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We present a machine learning approach to solve electronic quantum transport equations of one-dimensional nanostructures. The transmission coefficients of disordered systems were computed to provide training and test data sets to the machine. The system’s representation encodes energetic as well as geometrical information to characterize similarities between disordered configurations, while the Euclidean norm is used as a measure of similarity. Errors for out-of-sample predictions systematically decrease with training set size, enabling the accurate and fast prediction of new transmission coefficients. The remarkable performance of our model to capture the complexity of interference phenomena lends further support to its viability in dealing with transport problems of undulatory nature.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1357487
Journal Information:
Physical Review, Vol. 89, Issue 23; ISSN 0031-899X
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 50 works
Citation information provided by
Web of Science

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Cited By (26)

Machine Learning, Quantum Chemistry, and Chemical Space book January 2017
Quantum Machine Learning in Chemical Compound Space journal March 2018
Deep learning for computational chemistry journal March 2017
Feature vector clustering molecular pairs in computer simulations journal July 2019
Crystal structure representations for machine learning models of formation energies journal April 2015
Machine learning and artificial neural network accelerated computational discoveries in materials science journal November 2019
Big–deep–smart data in imaging for guiding materials design journal September 2015
Machine learning bandgaps of double perovskites journal January 2016
Machine-learned approximations to Density Functional Theory Hamiltonians journal February 2017
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics journal January 2018
Electronic spectra from TDDFT and machine learning in chemical space journal August 2015
Metadynamics for training neural network model chemistries: A competitive assessment journal June 2018
Constant size descriptors for accurate machine learning models of molecular properties journal June 2018
Deep learning and the Schrödinger equation journal October 2017
Mapping and classifying molecules from a high-throughput structural database journal February 2017
Development of a machine learning potential for graphene text January 2018
Electronic spectra from TDDFT and machine learning in chemical space text January 2015
Machine learning for many-body physics: The case of the Anderson impurity model text January 2014
Crystal structure representations for machine learning models of formation energies text January 2015
Electronic Spectra from TDDFT and Machine Learning in Chemical Space text January 2015
Comparing molecules and solids across structural and alchemical space text January 2016
Machine-learned approximations to Density Functional Theory Hamiltonians preprint January 2016
Mapping and Classifying Molecules from a High-Throughput Structural Database preprint January 2016
Deep Learning for Computational Chemistry preprint January 2017
The TensorMol-0.1 Model Chemistry: a Neural Network Augmented with Long-Range Physics preprint January 2017
Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment text January 2017

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97 MATHEMATICS AND COMPUTING