Back to overview

Modeling electronic quantum transport with machine learning

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Lopez-Bezanilla Alejandro, von Lilienfeld O. Anatole,
Project From atomistic exploration of chemical compound space towards bio-molecular design: Quantum mechanical rational compound design (QM-RCD)
Show all

Original article (peer-reviewed)

Volume (Issue) 89(23)
Page(s) 235411
Title of proceedings PHYSICAL REVIEW B
DOI 10.1103/physrevb.89.235411


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.