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

Moses, Oyawale Adetunji; Chen, Wei; Adam, Mukhtar Lawan; Wang, Zhuo; Liu, Kaili; Shao, Junming; Li, Zhengsheng; Li, Wentao; Wang, Chensu; Zhao, Haitao; Pang, Cheng Heng; Yin, Zongyou; Yu, Xue‐Feng

doi:10.1016/j.matre.2021.100049

Cited by 14 publications

(12 citation statements)

References 169 publications

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“…Furthermore, new high-throughput experimental platforms are emerging using robotics to reduce human interventions and experimental errors. 54,55 All of these approaches are efficient and effective in terms of their respective applications. However, more generalizability and physical insights from the atomic level and more efficient prediction with implications of new intelligent ML approaches are highly desirable.…”

Section: Challenges and Future Aspectsmentioning

confidence: 99%

A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

et al. 2023

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Machine learning (ML) with its indigenous predicting ability has been influential in the current scientific world and has enabled a paradigm shift in the field of CO2 reduction reaction (CO2RR). In this perspective, current research progress of ML approaches in heterogeneous electrocatalytic CO2RR has been demonstrated. The important findings related to the ML systems comprising features, output descriptors, and ML models have been summarized. Further, the opportunities and challenges in using the state-of-the-art ML methodologies along with the ways of circumventing those challenges are discussed. Finally, the interpretation of black box ML models and extensive usages of interpretable glass box and gray box models for CO2RR are encouraged for obtaining proper physical interpretations. The future directions on utilizing several such evolving ML methods to predict catalytic activity descriptors can help in a broader way to explore novel and efficient heterogeneous CO2RR and other similar catalytic reactions.

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Section: Challenges and Future Aspectsmentioning

confidence: 99%

A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

et al. 2023

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“…Therefore, designing materials directly by trial and error is costly. Interestingly, the emergence of high‐throughput experiments performed by robotic chemists, 49 chemical synthesis machines, 62 and a programmable system of materials synthesis 63 could pave a new way for the generation of high‐quality datasets. It is worth looking forward that the development of ML‐accelerated computing mentioned above and the emergence of high‐throughput automated experimental platforms will realize the enrichment of various datasets. Descriptor compatibility.…”

Section: Challenges Of ML In Electrocatalyst For Co2 Reductionmentioning

confidence: 99%

Machine learning accelerated calculation and design of electrocatalysts for CO₂ reduction

et al. 2022

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In the past decades, machine learning (ML) has impacted the field of electrocatalysis. Modern researchers have begun to take advantage of ML-based data-driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design. Hence, significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO 2 reduction. This review discusses recent applications of ML to discover, design, and optimize novel electrocatalysts. First, insights into ML aided in accelerating calculation are presented. Then, ML aided in the rational design of the electrocatalyst is introduced, including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model. Finally, the opportunities and future challenges are summarized for the future design of electrocatalyst for CO 2 reduction with the assistance of ML.

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“…[93][94][95] In fact, MLassisted robots can help to accelerate high-throughput experimentation without human interactions. [96][97][98][99] As a result, a MLguided robot was used to carry out 688 experiments within an experimental space of ten variables, 1000 times faster than manual approaches. The ML-assisted high-throughput experimentation revealed a new photocatalyst mixture with six times more activity.…”

Section: Integration Of ML With Experimentsmentioning

confidence: 99%

Machine learning for design principles for single atom catalysts towards electrochemical reactions

et al. 2022

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

Cited by 14 publications

References 169 publications

A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

Machine learning accelerated calculation and design of electrocatalysts for CO₂ reduction

Machine learning for design principles for single atom catalysts towards electrochemical reactions

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

Cited by 14 publications

References 169 publications

A Route Map of Machine Learning Approaches in Heterogeneous CO2 Reduction Reaction

A Route Map of Machine Learning Approaches in Heterogeneous CO2 Reduction Reaction

Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Contact Info

Product

Resources

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A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

A Route Map of Machine Learning Approaches in Heterogeneous CO₂ Reduction Reaction

Machine learning accelerated calculation and design of electrocatalysts for CO₂ reduction