Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane

Ishioka, Sora; Fujiwara, Aya; Nakanowatari, Sunao; Takahashi, Lauren; Taniike, Toshiaki; Takahashi, Keisuke

doi:10.1021/acscatal.2c03142

Cited by 14 publications

(7 citation statements)

References 22 publications

(32 reference statements)

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“…One is to accelerate the techniques of high-throughput experiments, as well as combine them with advanced characterization methods to obtain real sample data in large quantities. 179 On the other hand, data mining algorithms can also be used to extract data from existing published papers, and these data come from real materials and real test conditions. 180 Of course, we still need to explore how to integrate "real data" from different data sources, which places high demands on data preprocessing and standardization procedures.…”

Section: Databasementioning

confidence: 99%

Data-driven design of electrocatalysts: principle, progress, and perspective

et al. 2023

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Section: Databasementioning

confidence: 99%

Data-driven design of electrocatalysts: principle, progress, and perspective

et al. 2023

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“…表示催化活性的催化剂描述符在机器学习方面一直具有挑战性. Taniike 等 [113] 利用机器学习设计了甲烷氧化偶联(OCM)反应的催化剂描述符, 揭示了在该反应中代表乙烯/乙烷选择性(C 2 s)的五个关键物理量, 其中机器学习预测了三种催化剂具有较高的 C 2 s 值. 实验结果也证明了这一点, 催化反应过程中仍能保持其活性和选择性.…”

Section: 催化剂材料unclassified

Recent Advance of Machine Learning in Selecting New Materials

Qi¹,

Hu²,

Wang³

et al. 2023

Acta Chimica Sinica

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The new material industry is the foundation of technological change in many related fields, and also the forerunner of the development of new energy, aerospace, electronic information and other high-tech industries. Traditional means cannot meet the development needs of modern society because of disadvantages such as high cost, low efficiency and long commercial cycle. In recent years, with the application of big data combined with artificial intelligence in a deeper degree, data-driven machine learning has made great progress in the design, screening and performance prediction of new materials, which has greatly promoted the development and application of new materials. In this review, the basic process of machine learning, the algorithms commonly used in materials science and the relevant materials database are summarized. This review focuses on the application of machine learning in different functions, as well as the performance prediction in the fields of catalyst materials, lithium-ion batteries, semiconductor materials and alloy materials, presenting the latest progress in materials development. Finally, machine learning in the application of new materials are analyzed and prospected.

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“…Web-based visualization tools to deploy exploratory data analysis on HTE data using co-ordinated multiple views (CMVs) to discover apparent trends in the reaction performance across a variety of catalysts and operating conditions can provide insights for future experimentation [7]. Sophisticated ML tools to uncover the not so apparent insights require quantitative descriptors of a catalyst from elemental properties (atomic numbers, electron affinity, ionization energy, density) of constituent metal atoms from the periodic table to characterize its activity [8], or HTC-based reaction energetics descriptors from density functional theory [9,10]. Once the catalyst design space has been quantified by descriptors, unsupervised clustering can be used to identify catalyst groupings based on how they impact reaction performance, across different experimental conditions [11].…”

Section: Of 20mentioning

confidence: 99%

Model-Based Catalyst Screening and Optimal Experimental Design for the Oxidative Coupling of Methane

Puliyanda

2023

Preprint

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The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)), and deploys it to locate optimal conditions that maximize C2 yield for a catalyst. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions, while the predictions of methane conversion are driven by the reaction conditions. The machine learning (ML) regressor is used as a surrogate to develop performance curves for each of the catalysts via a multi-objective optimization routine that seeks to maximize the KPIs in the decision space of reaction conditions, is seen to locate optimal conditions at which the maximum C2 yields for catalysts are predicted to be 15%, higher on average. Analyzing the catalysts in the space of their performance curves with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, reveals distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts.

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Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane

Cited by 14 publications

References 22 publications

Data-driven design of electrocatalysts: principle, progress, and perspective

Data-driven design of electrocatalysts: principle, progress, and perspective

Recent Advance of Machine Learning in Selecting New Materials

Model-Based Catalyst Screening and Optimal Experimental Design for the Oxidative Coupling of Methane

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