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Issue 72, 2020
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A neural network-based approach to predicting absorption in nanostructured, disordered photoelectrodes

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Abstract

Disordered nanostructures in photoelectrodes can increase light absorption in photoelectrochemical system designs. Predicting their optical properties is an elusive task due to the immensity of unique configurations and the intrinsic variance of each. A neural network trained from a small subset of simulations can emulate the complex absorption properties of the entire configuration space for a model disordered system with quantifiable accuracy and computational efficiency.

Graphical abstract: A neural network-based approach to predicting absorption in nanostructured, disordered photoelectrodes

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Supplementary files

Article information


Submitted
19 Jun 2020
Accepted
29 Jul 2020
First published
05 Aug 2020

Chem. Commun., 2020,56, 10473-10476
Article type
Communication

A neural network-based approach to predicting absorption in nanostructured, disordered photoelectrodes

R. H. Coridan, Chem. Commun., 2020, 56, 10473
DOI: 10.1039/D0CC04229C

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