Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics

Atsushi Ishikawa

doi:https://doi.org/10.1038/s41598-022-15586-9

Publication Information

Title

Japanese:	Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics
English:	Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics

Author

Japanese:	石川敦之.
English:	Atsushi Ishikawa.

Language

English

Journal/Book name

Japanese:	Scientific Reports
English:	Scientific Reports

Volume, Number, Page

Vol. 12 No. 1

Published date

July 8, 2022

Publisher

Japanese:
English:

Conference name

Japanese:
English:

Conference site

Japanese:
English:

Official URL

http://dx.doi.org/10.1038/s41598-022-15586-9

DOI

https://doi.org/10.1038/s41598-022-15586-9

Abstract

AbstractMicrokinetic analysis based on density functional theory (DFT) was combined with a generative adversarial network (GAN) to enable the artificial proposal of heterogeneous catalysts based on the DFT-calculated dataset. The approach was applied to the NH3 formation reaction on Rh−Ru alloy surfaces as an example. The NH3 formation turnover frequency (TOF) was calculated by DFT-based microkinetics. Six elementary reactions, namely, N2 dissociation, H2 dissociation, NHx (x = 1–3) formation, and NH3 desorption, were explicitly considered, and their reaction energies were evaluated by DFT calculations. Based on the TOF values and atomic compositions, new alloy surfaces were generated using the GAN. This approach successfully generated the surfaces that were not included in the initial dataset but exhibited higher TOF values. The N2 dissociation reaction was more exothermic for the generated surfaces, leading to higher TOF. The present study demonstrates that the automatic improvement of catalyst materials is possible using DFT calculations and GAN sample generation.

Home

Search

Support

About T2R2

Related Links

Publication Information