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

Atsushi Ishikawa

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

論文・著書情報

タイトル

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

著者

和文:	石川敦之.
英文:	Atsushi Ishikawa.

言語

English

掲載誌/書名

和文:	Scientific Reports
英文:	Scientific Reports

巻, 号, ページ

Vol. 12 No. 1

出版年月

2022年7月8日

出版者

和文:
英文:

会議名称

和文:
英文:

開催地

和文:
英文:

公式リンク

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

DOI

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

アブストラクト

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.

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