Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data

Takahashi, Keisuke; Takahashi, Lauren; Nguyen, Thanh Nhat; Thakur, Ashutosh; Taniike, Toshiaki

doi:10.1021/acs.jpclett.0c01926

Cited by 26 publications

(23 citation statements)

References 24 publications

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“…By incorporating the fundamentals of kinetics and catalysis in statistical tools, the latter tools can be much simpler in nature than the ones developed in the era of machine learning. In practice, unsupervised machine learning techniques [78] will most likely be of greater use than deep learning. Further inspiration can be taken from earlier analogous work in chemical engineering, such as hybrid models [29] …”

Section: Kinetic Catalytic Data At the Foundation Of Digital Heterogementioning

confidence: 99%

Open Data in Catalysis: From Today's Big Picture to the Future of Small Data

et al. 2020

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Open science and data are yet to make a real breakthrough and research policies will have a critical role in it. The history and general context around open data is hence firstly addressed, including how researchers perceive the existing incentives, leading to recommendations on how to foster data sharing. Subsequently, the focus is on catalysis, with a particular emphasis on benchmarking the data sharing practices against other fields and surveying the type of data currently being shared. The current infrastructure, including data repositories, and standards formats is maped. The striking differences among different disciplines are discussed, serving as a basis to propose specific actions to promote data sharing in catalysis. Short‐term initiatives are needed to boost the amount of openly available data, particularly in heterogeneous catalysis, but a high degree of standardization in data formats will be needed to ensure optimal and automated data mining in the long run. Because of its unique, central role in understanding the catalytic action, kinetic catalytic data is of particular interest. As formats and mining tools are dependant on the type of data, kinetic catalytic data is firstly characterized. Guidelines for a standardized sharing format are proposed, taking into account the small, well‐structured nature of this type of data. To maximize the extraction of information, the low volume of kinetic catalytic data will be compensated by incorporating fundamental knowledge into statistics‐based tools. Whencoupled with knowledge generation tools, i. e. kinetic models, new insights at the active site and mechanism levels will be reached in an ever more automated and powerful way.

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Section: Kinetic Catalytic Data At the Foundation Of Digital Heterogementioning

confidence: 99%

Open Data in Catalysis: From Today's Big Picture to the Future of Small Data

et al. 2020

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“…[24] These approaches are expected to contribute to proving future principles of the present ML prediction for designing catalysts, particularly the OCM reaction. [25]…”

Section: Category (Index Color)mentioning

confidence: 99%

“…Very recently, our research team has successfully designed a high‐throughput fixed‐bed flow reactor system, which generated fine experimental datasets with the same reaction protocol [24] . These approaches are expected to contribute to proving future principles of the present ML prediction for designing catalysts, particularly the OCM reaction [25] …”

Section: Figurementioning

confidence: 99%

Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data

et al. 2020

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Machine learning (ML) predictions for the oxidative coupling of methane (OCM) are evaluated under experiment situation. The ML protocol has sparked new motivation for trial runs of 96 kinds of metal‐supported catalysts based not only on scientists’ experiences but also on data presented in earlier reports of the literatures and obtained during verification. Our protocol discovers unreported catalyst combinations for OCM reactions from data expanding upon three decades of research, where various numbers of catalysts are predicted and confirmed to perform better than blank data. Nevertheless, the target on C2 yield for the OCM reaction remains as a challenging subject: i. e. higher than 30 %. Revisiting data reported in the literature reveals that different reactor systems and/or specific methods are used in the original data for achieving higher than 30 % C2 yield. Such specialties are attributed to the inadequacy of a literature‐data‐driven ML approach at the present situation. Furthermore, classification of experimental data has indicated target C2 yield values and trends toward CH4 and O2 conversion and product selectivity in high dimensions can improve future ML prediction. These findings are greatly beneficial for the next stage of development to find a global descriptor to improve ML prediction accuracy beyond interpolation filling.

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“…The OCM reaction is one of the most studied heterogeneous catalytic reactions using ML and other relevant statistical analysis techniques with datasets obtained from high‐throughput experiments, published data, and computational studies [36–49] . Many of these studies have utilized the database of Baerns and coworkers, which consists of 1868 OCM reaction datapoints, includes the catalyst composition, experimental conditions, and the catalytic performance of the reactions, and was compiled from a wide range of data published before 2010 [42] .…”

Section: Introductionmentioning

confidence: 99%

Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts

et al. 2021

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We have constructed and analyzed an updated dataset consisting of 4759 experimental datapoints for the oxidative coupling of methane (OCM) reaction based on literature data reported before 2020 (∼2019) using machine learning (ML) methods. Several ML methods, including random forest regression (RFR), extra trees regression (ETR), and gradient boosting regression with XGBoost (XGB), were used in conjunction with our proposed approach, in which elemental features are used as input representations rather than inputting the catalyst compositions directly. A recent research trend, namely, the extensive exploration of Mn/Na2WO4/SiO2 catalyst systems in recent years due to their high activity and durability, was clearly reflected in the dataset analysis. An ML model for the prediction of the reaction outcome (C2 yield) was successfully developed, and feature importance scores and SHapley Additive exPlanations (SHAP) values were calculated based on ETR and XGB, respectively, to identify the input variables with the greatest influence on the catalyst performance and observe how these important variables affect the C2 yield in the OCM. The discovery and optimization of catalytic processes using ML as a “surrogate” model were explored, and promising catalytic system candidates for the OCM reaction were identified. Notably, the developed ML model predicted catalysts containing elements that do not appear in the OCM dataset. This clearly demonstrates desirably high potential of our ML model to enable extrapolative predictions for ML‐aided future catalysis research.

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Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data

Cited by 26 publications

References 24 publications

Open Data in Catalysis: From Today's Big Picture to the Future of Small Data

Open Data in Catalysis: From Today's Big Picture to the Future of Small Data

Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data

Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts

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