High-throughput screening of bimetallic catalysts enabled by machine learning

Li, Zheng; Wang, Siwen; Chin, Wei Shan; Achenie, Luke E.K.; Xin, Hongliang

doi:10.1039/c7ta01812f

Cited by 292 publications

(300 citation statements)

References 50 publications

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“…This low error is within the screening energy range of potential catalysts. Similar features have been used in the prediction of CO and OH adsorption on bimetallic surfaces to identify several transition metal alloys and local environments with theoretical performance better than Pt in direct methanol fuel cells . Similarly, Gasper et al have studied the CO adsorption on the Pt nanoparticles using a ML approach.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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“…Predictive models of adsorption energies of common catalytic descriptors like OH*, CO*, or NO* on multimetallic surfaces have flourished in the past years. These models can be roughly assigned to physics‐based and data‐driven approaches. While the former approaches start their predictions from pre‐determined physical models, like the d ‐band theory, bond order conservation, or electrostatic interactions, the latter use large sets of materials properties and try to identify and use trends that might not have a known analytical expression .…”

Section: Recent Trendsmentioning

confidence: 99%

“…Ma et al . and Li et al . trained artificial neural networks with sets of DFT‐calculated adsorption energies and electronic structure fingerprints like e. g. moments of the d ‐band distribution.…”

Section: Recent Trendsmentioning

confidence: 99%

Machine Learning for Computational Heterogeneous Catalysis

et al. 2019

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Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

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“…As outlined in Section , having a physically relevant and easily accessible features is crucial to build models for catalyst screening applications as data representation could dramatically influence ML performance . In another study, p‐band and d‐band characteristics of an adsorption site were found as the key features describing the capabilities of bimetallics in breaking energy‐scaling constraints of *CO . A noticeable contribution was found from d‐band shape and sp‐band filling, which is typically ignored in simplistic d‐band model.…”

Section: Machine Learning In Co2rrmentioning

confidence: 99%

A Disquisition on the Active Sites of Heterogeneous Catalysts for Electrochemical Reduction of CO₂ to Value‐Added Chemicals and Fuel

Daiyan

Saputera

Masood

et al. 2020

Advanced Energy Materials

128

104

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Renewable‐electricity‐powered electrocatalytic CO2 reduction reactions (CO2RR) have been identified as an emerging technology to address the issue of rising CO2 emissions in the atmosphere. While the CO2RR has been demonstrated to be technically feasible, further improvements in catalyst performance through active sites engineering are a prerequisite to accelerate its commercial feasibility for utilization in large CO2‐emitting industrial sources. Over the years, the improved understanding of the interaction of CO2 with the active sites has allowed superior catalyst design and subsequent attainment of prominent CO2RR activity in literature. This review tracks the evolution of the understanding of CO2RR active sites on different electrocatalysts such as metals, metal‐oxides, single atoms, metal‐carbon, and subsequently metal‐free carbon‐based catalysts. Despite the tremendous research efforts in the field, many scientific questions on the role of various active sites in governing CO2RR activity, selectivity, stability, and pathways are still unanswered. These gaps in knowledge are highlighted and a discussion is set forth on the merits of utilizing advanced in‐situ and operando characterization techniques and machine learning (ML). Using this technique, the underlying mechanisms can be discerned, and as a result new strategies for designing active sites may be uncovered. Finally, this review advocates an interdisciplinary approach to discover and design CO2RR active sites (rather than focusing merely on catalyst activity) in a bid to stimulate practical research for industrial application.

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High-throughput screening of bimetallic catalysts enabled by machine learning

Abstract: We present a holistic machine-learning framework for rapid screening of bimetallic catalysts with the aid of the descriptor-based kinetic analysis.

Cited by 292 publications

References 50 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Machine Learning for Computational Heterogeneous Catalysis

A Disquisition on the Active Sites of Heterogeneous Catalysts for Electrochemical Reduction of CO₂ to Value‐Added Chemicals and Fuel

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High-throughput screening of bimetallic catalysts enabled by machine learning

Abstract: We present a holistic machine-learning framework for rapid screening of bimetallic catalysts with the aid of the descriptor-based kinetic analysis.

Cited by 292 publications

References 50 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Machine Learning for Computational Heterogeneous Catalysis

A Disquisition on the Active Sites of Heterogeneous Catalysts for Electrochemical Reduction of CO2 to Value‐Added Chemicals and Fuel

Contact Info

Product

Resources

About

A Disquisition on the Active Sites of Heterogeneous Catalysts for Electrochemical Reduction of CO₂ to Value‐Added Chemicals and Fuel