Combinatorial optimization and synthesis of multiple promoted MoVNbTe catalysts for oxidation of propane to acrylic acid

Tompos, András; Sanchez‐Sanchez, Maricruz; Végvári, Lajos; Szijjártó, Gábor P.; Margitfalvi, József L.; Trunschke, Annette; Schlögl, Robert; Wanninger, Klaus; Mestl, Gerhard

doi:10.1016/j.cattod.2019.03.047

Cited by 23 publications

(11 citation statements)

References 26 publications

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“…Machine learning-based techniques such as genetic algorithms (GA) and artificial neural networks (ANN) have also been used for a long time to gain insights from prior high-throughput experiments for designing the next generations. [101][102][103][104] Great potential is seen in the better integration of high-throughput experiments and data science. 19,35 The progressive development of machine learning and robotics could fundamentally change basic research and significantly accelerate progress.…”

Section: Automation Of Unit Operationsmentioning

confidence: 99%

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Trunschke

2022

Catal. Sci. Technol.

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Section: Automation Of Unit Operationsmentioning

confidence: 99%

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Trunschke

2022

Catal. Sci. Technol.

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“…Around the turn of the century, high-throughput experimentation became a frequently applied method to screen for both new catalyst compositions and optimized reaction conditions [107][108][109][110]. 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].…”

Section: Implementation Of Handbooksmentioning

confidence: 99%

“…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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“…Another approach for identifying novel catalysts consists of the use of high-throughput experimentation (HTE) to test large numbers of materials. 3 However, utilizing the information obtained by the experiments to decide on the next promising materials to investigate is not straightforward. 4 As the number of possible materials is practically infinite and the number of good catalysts is very small, the direct approach is unlikely to identify the needed catalyst material.…”

Section: Introductionmentioning

confidence: 99%

“…While theoretical approaches attempt to address the complexity of heterogeneous catalysis, the explicit atomistic modeling of the full catalytic progression by first-principles methods is impractical. Another approach for identifying novel catalysts consists of the use of high-throughput experimentation (HTE) to test large numbers of materials . However, utilizing the information obtained by the experiments to decide on the next promising materials to investigate is not straightforward .…”

Section: Introductionmentioning

confidence: 99%

Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence

et al. 2022

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The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small in comparison to the number of possible materials. Here, we show how the subgroup-discovery (SGD) artificial-intelligence approach can be applied to an experimental plus theoretical data set to identify constraints on key physicochemical parameters, the so-called SG rules , which exclusively describe materials and reaction conditions with outstanding catalytic performance. By using high-throughput experimentation, 120 SiO 2 -supported catalysts containing ruthenium, tungsten, and phosphorus were synthesized and tested in the catalytic oxidation of propylene. As candidate descriptive parameters, the temperature and 10 parameters related to the composition and chemical nature of the catalyst materials, derived from calculated free-atom properties, were offered. The temperature, the phosphorus content, and the composition-weighted electronegativity are identified as key parameters describing high yields toward the value-added oxygenate products acrolein and acrylic acid. The SG rules not only reflect the underlying processes particularly associated with high performance but also guide the design of more complex catalysts containing up to five elements in their composition.

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Combinatorial optimization and synthesis of multiple promoted MoVNbTe catalysts for oxidation of propane to acrylic acid

Cited by 23 publications

References 26 publications

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Towards Experimental Handbooks in Catalysis

Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence

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