Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions

Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions

Technological advancements in recent decades have greatly transformed the field of material chemistry. Juxtaposing the accentuating energy demand with the pollution associated, urgent measures are required to ensure energy maximization, while reducing the extended experimental time cycle involved in...

Full description

Bibliographic Details
Main Authors: Oyawale Adetunji Moses, Wei Chen, Mukhtar Lawan Adam, Zhuo Wang, Kaili Liu, Junming Shao, Zhengsheng Li, Wentao Li, Chensu Wang, Haitao Zhao, Cheng Heng Pang, Zongyou Yin, Xuefeng Yu
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2021-08-01
Series:Materials Reports: Energy
Subjects:
Material chemistry
Sustainable energy
Artificial intelligence
Machine learning models
Robots
Catalyst discovery
Online Access:http://www.sciencedirect.com/science/article/pii/S2666935821000847

Similar Items

Similar Items