High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane

Article Engineering, Chemical

A chemistry-inspired neural network kinetic model for oxidative coupling of methane from high-throughput data

Kexin Chen et al.

Summary: A neural network kinetic model is developed to study the catalyst for oxidative coupling of methane. The model evaluates the reaction kinetics and sensitivity of the catalyst, MnNa2WO4/SiO2, to changes in different components.

AICHE JOURNAL (2022)

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Article Chemistry, Applied

Dissecting La2Ce2O7 catalyst to unravel the origin of the surface active sites devoting to its performance for oxidative coupling of methane (OCM)

Zhixuan Zhang et al.

Summary: By analyzing the reaction performance of pure oxides and mixtures, it is found that La2Ce2O7 compound exhibits superior OCM performance. Characterization experiments conducted on this compound reveal the presence of a large amount of O2- species and moderate basic sites on its surface, leading to enhanced reaction performance.

CATALYSIS TODAY (2022)

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Article Energy & Fuels

In-depth understanding of the crystal-facet effect of La2O2CO3 for low-temperature oxidative coupling of methane

Ru Feng et al.

Summary: This study found that La2O2CO3 nanoparticles with {001} facets exhibit higher activity in the low-temperature oxidative coupling of methane (OCM) reaction, but show lower selectivity due to excessive dissociation and deep oxidation.

FUEL (2022)

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Review Chemistry, Physical

Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

M. Erdem Gunay et al.

Summary: In recent years, the use of machine learning in catalysis has significantly increased due to advances in data processing technologies and the accumulation of a wealth of data in published literature and databases. Researchers analyze data using various machine learning techniques to discover knowledge, develop prediction models, and derive rules for the future. This communication aims to review works involving knowledge discovery in catalysis using machine learning techniques, while also summarizing the basic principles, common tools, and implementation of machine learning in catalysis.

CATALYSIS REVIEWS-SCIENCE AND ENGINEERING (2021)

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Article Chemistry, Physical

Direct Design of Catalysts in Oxidative Coupling of Methane via High-Throughput Experiment and Deep Learning

Kanami Sugiyama et al.

Summary: The combination of deep learning and high-throughput experiments is proposed for the direct design of heterogeneous catalysts in the oxidative coupling of methane (OCM) reaction. Through prediction and evaluation, a highly active unreported catalyst was discovered, providing a new approach for catalyst design.

CHEMCATCHEM (2021)

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Article Chemistry, Physical

Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning

Itsuki Miyazato et al.

Summary: Multioutput support vector regression is utilized to simultaneously predict selectivities and CH4 conversion in different catalysts, unveiling the effects of Mn, Ti, and Pd, and identifying trade-off points for CO and C2H6 to optimize C2H6 yield. This simultaneous prediction helps understand catalyst activities and provides guidance in experimental design.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

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Article Chemistry, Physical

Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane

Thanh Nhat Nguyen et al.

Summary: Combinatorial catalyst design is not easily generalizable, but random sampling of 300 quaternary solid catalysts in a materials space of 36,540 catalysts identified 51 catalysts with significantly better performance in the oxidative coupling of methane. Data analysis highlighted the importance of choosing synergistic combinations, which could be generalized based on the periodic table. Decision tree classification successfully facilitated efficient sampling of quaternary catalysts for improved C-2 yield.

ACS CATALYSIS (2021)

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Review Engineering, Chemical

Experimental methods in chemical engineering: High throughput catalyst testing - HTCT

Carlos Ortega et al.

Summary: High-throughput catalyst testing (HTCT) utilizes multiple parallel reactors and can reduce experimental time by two orders of magnitude, decreasing variance and quantifying random errors. This approach requires precise dosing, temperature control, isothermal zone identification, online gas-phase analysis, and a common back pressure regulator for constant pressure maintenance.

CANADIAN JOURNAL OF CHEMICAL ENGINEERING (2021)

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Article Chemistry, Physical

Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane

Sunao Nakanowatari et al.

Summary: Through analyzing a catalyst dataset, it was found that using a mixed support of La2O3 and BaO helps balance the activity and selectivity in OCM reaction, leading to improved yield.

CHEMCATCHEM (2021)

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Article Chemistry, Physical

Factors to influence low-temperature performance of supported Mn-Na2WO4 in oxidative coupling of methane

Thanh Nhat Nguyen et al.

Summary: The performance of Mn-Na2WO4 catalysts in oxidative coupling of methane, highly depends on the choice of support materials. Various Mn-Na2WO4 catalysts supported on different Si-based materials were prepared and evaluated using a high-throughput screening instrument. The relationship between catalyst performance and support materials was studied in detail through characterization techniques including Raman spectroscopy, X-ray diffraction, elemental mapping, and X-ray photoelectron spectroscopy.

MOLECULAR CATALYSIS (2021)

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Article Chemistry, Physical

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

Shinya Mine et al.

Summary: An updated dataset of 4759 experimental datapoints for the OCM reaction was analyzed using machine learning methods, highlighting the trend of Mn/Na2WO4/SiO2 catalyst systems. A predictive ML model was successfully developed, identifying potential catalytic candidates for future research. Promisingly, the ML model predicted catalysts with elements not present in the original dataset, showcasing its potential for extrapolative predictions in future catalysis research.

CHEMCATCHEM (2021)

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Article Chemistry, Multidisciplinary

Unveiling gas-phase oxidative coupling of methane via data analysis

Sora Ishioka et al.

Summary: By combining catalysts informatics with high-throughput experimental data, this study explores the oxidative coupling of methane (OCM) reaction mechanism. Pairwise correlation and data visualization are used to uncover the relationship between reaction conditions and selectivity/conversion, with machine learning filling in the gaps between experimental data points to provide a more detailed understanding of the OCM reaction against different reaction conditions. Ultimately, catalysts informatics is proposed as a tool to aid in understanding the intricate details of reaction mechanisms and optimizing reaction design.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2021)

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Article Chemistry, Physical

Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts

Keisuke Takahashi et al.

Summary: This method introduces a new way of describing catalysts through catalyst gene expression profiling to identify similar catalysts and design new catalysts. By combining constructed catalyst gene sequences with hierarchical clustering, catalyst gene expression profiling enables the recognition of similarities in catalysts and catalytic activity, allowing for the design of new catalysts with experimentally confirmed catalytic activities associated with their catalyst gene sequences.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

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Article Chemistry, Multidisciplinary

Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

Lauren Takahashi et al.

Summary: Research on designing high-performance catalysts for the oxidative coupling of methane is often hindered by inconsistent data, but high throughput experiments provide a systematic way to produce catalyst-related data. By applying graph theory to visualize trends in data transformation, new catalysts can be designed to achieve high C-2 yields, resulting in the successful design of numerous efficient catalysts.

CHEMICAL SCIENCE (2021)

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Review Chemistry, Physical

Constructing Ni-based confinement catalysts with advanced performances toward the CO2 reforming of CH4: state-of-the-art review and perspectives

Yingying Xue et al.

Summary: The global greenhouse effect is receiving more attention due to the increasing emission of greenhouse gases, prompting the development of efficient Ni-based confinement catalysts to address the challenges of thermal deactivation and surface carbon deposition during CO2 reforming of CH4. These catalysts aim to stabilize Ni active sites and inhibit carbon deposition through various confinement structures, showing advanced performance for CRM.

CATALYSIS SCIENCE & TECHNOLOGY (2021)

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Article Chemistry, Physical

Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining

Junya Ohyama et al.

Summary: By utilizing machine learning and data mining, a direct design of low temperature oxidative coupling of methane (OCM) catalysts was achieved, revealing hidden physical rules behind catalysis and leading to the discovery of new catalysts.

CATALYSIS SCIENCE & TECHNOLOGY (2021)

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Review Chemistry, Applied

Tailoring La2Ce2O7 catalysts for low temperature oxidative coupling of methane by optimizing the preparation methods

Yan Zhang et al.

CATALYSIS TODAY (2020)

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Article Chemistry, Physical

High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane

Thanh Nhat Nguyen et al.

ACS CATALYSIS (2020)

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Review Chemistry, Physical

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Takashi Toyao et al.

ACS CATALYSIS (2020)

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Article Chemistry, Physical

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

Keisuke Takahashi et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

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Article Chemistry, Physical

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

Shun Nishimura et al.

CHEMCATCHEM (2020)

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Article Chemistry, Physical

Particle Size Effects in Stoichiometric Methane Combustion: Structure-Activity Relationship of Pd Catalyst Supported on Gamma-Alumina

Jianjun Chen et al.

ACS CATALYSIS (2020)

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Article Chemistry, Multidisciplinary

Catalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts

Jun Fujima et al.

REACTION CHEMISTRY & ENGINEERING (2020)

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Article Engineering, Chemical

The role of mass and heat transfer in the design of novel reactors for oxidative coupling of methane

Laurien A. Vandewalle et al.

CHEMICAL ENGINEERING SCIENCE (2019)

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Article Multidisciplinary Sciences

A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction

Roman Schmack et al.

NATURE COMMUNICATIONS (2019)

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Article Chemistry, Physical

Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

Junya Ohyama et al.

CHEMCATCHEM (2019)

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Article Multidisciplinary Sciences

Unsupervised word embeddings capture latent knowledge from materials science literature

Vahe Tshitoyan et al.

NATURE (2019)

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Article Chemistry, Physical

The Rise of Catalyst Informatics: Towards Catalyst Genomics

Keisuke Takahashi et al.

CHEMCATCHEM (2019)

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Article Chemistry, Physical

Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data

Keisuke Takahashi et al.

CHEMCATCHEM (2018)

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Article Chemistry, Applied

Fast Optimization of LiMgMnOx/La2O3 Catalysts for the Oxidative Coupling of Methane

Zhinian Li et al.

ACS COMBINATORIAL SCIENCE (2017)

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Article Chemistry, Physical

Methane conversion into different hydrocarbons or oxygenates: current status and future perspectives in catalyst development and reactor operation

Evgenii V. Kondratenko et al.

CATALYSIS SCIENCE & TECHNOLOGY (2017)

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Review Chemistry, Physical

Machine learning in materials informatics: recent applications and prospects

Rampi Ramprasad et al.

NPJ COMPUTATIONAL MATERIALS (2017)

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Review Chemistry, Physical

Advanced reactor concepts for oxidative coupling of methane

A. Cruellas et al.

CATALYSIS REVIEWS-SCIENCE AND ENGINEERING (2017)

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Article Chemistry, Physical

Structure Sensitivity of La2O2CO3 Catalysts in the Oxidative Coupling of Methane

Yu-Hui Hou et al.

ACS CATALYSIS (2015)

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Article Chemistry, Physical

Feasibility study of the Mn-Na2WO4/SiO2 catalytic system for the oxidative coupling of methane in a fluidized-bed reactor

S. Sadjadi et al.

CATALYSIS SCIENCE & TECHNOLOGY (2015)

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Article Chemistry, Multidisciplinary

Breaking Through

CHEMICAL & ENGINEERING NEWS (2015)

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Article Chemistry, Physical

Ce-Doped La2O3 based catalyst for the oxidative coupling of methane

Victor J. Ferreira et al.

CATALYSIS COMMUNICATIONS (2013)

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Article Chemistry, Physical

Oxidative Coupling of Methane by Nanofiber Catalysts

Daniel Noon et al.

CHEMCATCHEM (2013)

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Article Chemistry, Multidisciplinary

Exploiting shape effects of La2O3 nanocatalysts for oxidative coupling of methane reaction

Ping Huang et al.

NANOSCALE (2013)

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Article Chemistry, Applied

Catalyst design based on microkinetic models: Oxidative coupling of methane

Joris W. Thybaut et al.

CATALYSIS TODAY (2011)

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Article Chemistry, Physical

Statistical Analysis of Past Catalytic Data on Oxidative Methane Coupling for New Insights into the Composition of High-Performance Catalysts

Ulyana Zavyalova et al.

CHEMCATCHEM (2011)

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Article Chemistry, Applied

High-temperature parallel screening of catalysts for the oxidative coupling of methane

Louis Olivier et al.

CATALYSIS TODAY (2008)

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Article Chemistry, Physical

Integrating high-throughput characterization into combinatorial heterogeneous catalysis: unsupervised construction of quantitative structure/property relationship models

A Corma et al.

JOURNAL OF CATALYSIS (2005)

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High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane

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