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
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AI 연구 키워드·방법
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 …
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
| 연도 | 과제 | 부처 | 사업 | 수행기관 | NTIS 분류(1순위) |
|---|---|---|---|---|---|
| 2022 | 지능형 다중스케일 생명화학공학 교육연구단 | 교육부 | 4단계두뇌한국21사업(R&D) | 한국과학기술원 | – |
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Machine Learning in Materials Science