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

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

Research output: Journal PublicationReview articlepeer-review

11 Citations (Scopus)


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 energy production. In lieu of this, the prominence of catalysts in chemical reactions, particularly energy related reactions cannot be undermined, and thus it is critical to discover and design catalyst, towards the optimization of chemical processes and generation of sustainable energy. Most recently, artificial intelligence (AI) has been incorporated into several fields, particularly in advancing catalytic processes. The integration of intensive data set, machine learning models and robotics, provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques. The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst, with extreme accuracy and swiftness comparable to a number of human researchers. Although, the utilization of robots in catalyst discovery is still in its infancy, in this review we summarize current sway of artificial intelligence in catalyst discovery, briefly describe the application of databases, machine learning models and robots in this field, with emphasis on the consolidation of these monomeric units into a tripartite flow process. We point out current trends of machine learning and hybrid models of first principle calculations (DFT) for generating dataset, which is integrable into autonomous flow process of catalyst discovery. Also, we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors.

Original languageEnglish
Article number100049
JournalMaterials Reports: Energy
Issue number3
Publication statusPublished - Aug 2021


  • Artificial intelligence
  • Catalyst discovery
  • Intensive dataset
  • Machine learning models
  • Material chemistry
  • Robots
  • Sustainable energy

ASJC Scopus subject areas

  • Materials Science (miscellaneous)
  • Energy (miscellaneous)
  • Materials Chemistry
  • Electrochemistry


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