SNU CBE

Heterogeneous Catalysis in Grammar School

2022 · The Journal of Physical Chemistry C

저자

Johannes T. Margraf, Zachary Ward Ulissi, 정유성 (Yousung Jung), Karsten Reuter

연구주제

Machine Learning in Materials Science · Catalytic Processes in Materials Science · Advanced Chemical Physics Studies

NTIS 과학기술표준분류

화공 > 화학공정 > 촉매/반응기술

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AI 연구 키워드·방법

촉매 발굴 불균일 촉매작용 solid catalyst

키워드 (OpenAlex)

heterogeneous catalysis generative grammar catalyst discovery solid catalysts catalyst stability computational materials design renewable energy synthetic fuels

초록

High Resolution Image Download MS PowerPoint Slide The discovery of new catalytically active materials is one of the holy grails of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources and reduce the energy consumption of chemical industry. Indeed, heterogeneous catalysis is essential for the production of synthetic fuels and many commodity chemicals. Consequently, novel solid catalysts with higher activity and selectivity, increased sustainability and longevity, or improved prospects for rejuvenation and cyclability are needed for a diverse range of processes. Unfortunately, computational catalyst discovery is a daunting task, among other reasons because it is often unclear whether a proposed material is stable or synthesizable. This perspective proposes a new approach to this challenge, namely the use of generative grammars. We outline …

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교수 1

정유성

연구주제 1

Machine Learning in Materials Science

정책분야 1

화학공정

키워드 2

불균일 촉매작용 · 촉매 발굴