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

Nguyen, Thanh Nhat; Nhat, Thuy Tran Phuong; Takimoto, Ken; Thakur, Ashutosh; Nishimura, Shun; Ohyama, Junya; Miyazato, Itsuki; Takahashi, Lauren; Fujima, Jun; Takahashi, Keisuke; Taniike, Toshiaki

doi:10.1021/acscatal.9b04293

Cited by 131 publications

(188 citation statements)

References 41 publications

(98 reference statements)

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“…Although deep learning has shown high performance, it has been difficult to implement deep learning in catalysts informatics due to the lack of catalysts data that is sufficient for deep learning as it requires tens of thousands of data [9] . Therefore, high‐throughput experiments would be the necessary component to acquire catalysts data not only sufficient in the amount but also covering variation of catalysts and experimental conditions [13–15] …”

Section: Data Science Methodsmentioning

confidence: 99%

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

et al. 2020

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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. Deep learning predicts 20 active catalysts from high‐throughput 12,708 OCM experimental data where 19 of the predicted 20 catalysts have not been previously reported. The predicted 20 catalysts are then evaluated through high‐throughput experiments where a highly active unreported catalyst Ti−Na2WO4/TiO2 is discovered. Ti−Na2WO4/TiO2 results in a high C2 yield of 18.8 % where the maximum C2 yield is reported to be approximately 20 % within the 12,708 OCM data. Furthermore, the experimental conditions predicted for Ti−Na2WO4/TiO2 are also reproduced by high‐throughput experiment. Thus, deep learning demonstrates that both catalysts and experimental conditions can be simultaneously explored for designing catalysts. More importantly, deep learning assisted catalysts search is found to dramatically increase the chances of finding active catalysts where 9 out of 20 predicted catalysts result in a C2 yield over 15 %. Therefore, the combination of deep learning with high‐throughput experiments is proposed to be an effective strategy for the direct design of catalysts.

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Section: Data Science Methodsmentioning

confidence: 99%

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

et al. 2020

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“…To facilitate screening, library design by genetic algorithm (GA) was implemented [111,112]. Decision trees and principal component analysis (PCA) were applied to extract large amount of data from the literature [113,114] and from high-throughput experiments, in some cases ending up with catalyst compositions and reaction conditions already known from experience [112,115]. Despite all efforts, no real breakthrough in the implementation of new technologies was achieved.…”

Section: Implementation Of Handbooksmentioning

confidence: 99%

Towards Experimental Handbooks in Catalysis

et al. 2020

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The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.

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“…Petrochemical Cracking [112,[114][115][116][117][118][119][120][121][122][123][124][125][126][127][128][129][130] Isomerization [101,112,[122][123][124][130][131][132][133][134] Small molecule formation [68,113,[135][136][137][138] Oxidation Ammoxidation [139] CO oxidation [140,141] Controlled Combustion [142] Epoxidation [69,143,144] Other industrially relevant processes Adsorption [83,145] N 2 O abatement [146][147][148][149][150][151]…”

Section: High Throughput Gas Chromatography Application Referencesmentioning

confidence: 99%

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

et al. 2020

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Notwithstanding these impediments, in many fields of materials science, solutions are being designed to mitigate hindrances to the efficient sampling of chemical space and improving the robustness of computational screening models through a combination of high throughput synthesis (HTS), characterization, and machine learning. In this review, we highlight key developments in high throughput approaches pertinent to porous materials. Specifically, we focus on developments in the field of zeolitic materials, metal-organic frameworks (MOFs), and touch upon covalent organic frameworks (COFs). Although superficially these classes of materials have little in common, there are structural links such as topology which allow for the consideration of how high throughput methods contrast with one another. By contrasting developments in different fields, we identify some underutilized approaches which could be leveraged in the future. We target structurally ordered materials because the exploration of chemical and topological space is more straightforward to enumerate, though similar approaches could be applied to disordered materials. The scale of the problem faced in materials science of targeted structure and function [1] is by no means unique; in drug discovery, HTS and chemoinformatic approaches have been used for more than 40 years in an attempt to accelerate the discovery of druggable molecules. [2,3] Unlike the drug discovery process, where Lipinski's "rule of 5" [4] provides a reliable top level sift for viable targets, identifying the most promising material for a target application requires very particular properties that may be intertwined in complex and contradictory ways. For example, thermoelectrics require high electrical conductivity and low thermal conductivity and yet these properties are typically correlated unless they can be separated. [5] Given the large scope of potential applications, the advent of the Materials Genome Initiative (MGI) [6,7] in the United States has undoubtedly helped to promote ways to solve the aforementioned obstacles and numerous others. Similarly there are major initiatives within the EU (e.g., NOMAD [8] and BIGmax [9]) and Switzerland (MARVEL [10]). In the MGI, nanoporous materials, including zeolites and MOFs, have been specifically targeted [11] and in this review, we seek to highlight particular challenges in the field of porous materials and how researchers have sought to overcome these challenges. We focus primarily on the methods used in HTS and rapid throughput/screening computational approaches. Aspects of high throughput computation applied to porous materials have been reviewed before, [12-14] as has high throughput experimentation (HTE) [15,16] but here we focus on their combination within an integrated workflow. Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre-and post-synthetic computer simulations are mostly carried out in a piecemeal a...

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

Cited by 131 publications

References 41 publications

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

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

Towards Experimental Handbooks in Catalysis

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

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