Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

Suzuki, Keisuke; Toyao, Takashi; Maeno, Zen; Takakusagi, Satoru; Shimizu, Ken‐ichi; Takigawa, Ichigaku

doi:10.1002/cctc.201901456

Cited by 11 publications

(11 citation statements)

References 74 publications

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“…65 Many practical examples will be covered in subsequent sections and existing reviews, but in section 7.1 (Figure 24) we also introduce an example from our group that is based on balancing this exploitation− exploration trade-off. 66 2.7. Bayesian Inference, Generative Models, and Inverse Design.…”

Section: Acs Catalysismentioning

confidence: 99%

“…189−193 Very recently, our group reported a statistical analysis and a proposal of novel heterogeneous catalysts for OCM using ML treatment of literature data. 66 This effort led to the development of a novel ML method considering elemental features as input representations instead of inputting catalyst compositions directly (Figure 23). Effective analysis of literature data by ML methods has the capability to provide valuable information.…”

Section: Acs Catalysismentioning

confidence: 99%

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Machine Learning for Catalysis Informatics: Recent Applications and Prospects

et al. 2019

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The discovery and development of catalysts and catalytic processes are essential components to maintaining an ecological balance in the future. Recent revolutions made in data science could have a great impact on traditional catalysis research in both industry and academia and could accelerate the development of catalysts. Machine learning (ML), a subfield of data science, can play a central role in this paradigm shift away from the use of traditional approaches. In this review, we present a user’s guide for ML that we believe will be helpful for scientists performing research in the field of catalysis and summarize recent progress that has been made in utilizing ML to create homogeneous and heterogeneous catalysts. The focus of the review is on the design, synthesis, and characterization of catalytic materials/compounds as well as their applications to catalyzed processes. The ML technique not only enhances ways to discover catalysts but also serves as a powerful tool to establish a deeper understanding of relationships between the properties of materials/compounds and their catalytic activities, selectivities, and stabilities. This knowledge facilitates the establishment of principles employed to design catalysts and to enhance their efficiencies. Despite such advantages of ML, it is noteworthly that the current ML-assisted development of real catalysts remains in its infancy, mainly because of the complexity of catalysis associated with the fact that catalysis is a time-dependent dynamic event. In this review, we discuss how seamless integration of experiment, theory, and data science can be used to accelerate catalyst development and to guide future studies aimed at applications that will impact society’s need to produce energy, materials, and chemicals. Moreover, the limitations and difficulties of ML in catalysis research originating from the complex nature of catalysis are discussed in order to make the catalysis community aware of challenges that need to be addressed for effective and practical use of ML in the field.

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Section: Acs Catalysismentioning

confidence: 99%

Section: Acs Catalysismentioning

confidence: 99%

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

et al. 2019

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“…379−381 Two additional critical gaps exist that hinder the implementation of data science: (i) unification of data and (ii) data storage, accessibility, and exchange. 371 This surge of activity in the area of data science was identified and highlighted as a priority research direction (PRD #5) in the "Basic Research Needs for Catalysis Science to Transform Energy Technologies" workshop, where the following key questions were formulated: "How do we augment hypothesis-based catalyst discovery with data science tools, including machine and deep learning, to extract new knowledge from highly diverse datasets? How can we use this approach to predict effective combinations of catalytic functions, structural components, reaction environments, and reaction mechanisms for complex systems?"…”

Section: Lessons Learned and Future Opportunitiesmentioning

confidence: 99%

“…We would be remiss not to briefly discuss the emerging field of informatics for catalysis, which is growing rapidly because of its potential to revolutionize the discovery and design of catalysts. Recently, ChemCatChem published a series of papers as a special collection highlighting the use of data science in catalysis. − Although computational chemists have long used informatics approaches, , the confluence of high-throughput synthesis, characterization, and testing of catalysts, and the proliferation of computational capabilities have driven the development of machine learning and data science tools . These tools are enabling the analysis of both experimental and computed data in an effort to discover hidden relationships and connect chemical properties with catalytic activity .…”

Section: Lessons Learned and Future Opportunitiesmentioning

confidence: 99%

“…These hidden correlations form the basis for new hypothesis-driven paradigms to guide catalysis in areas that have not been thoroughly explored. Electrochemically driven transformations of organic molecules fall squarely in this category and offer a prime target for data science strategies. − Two additional critical gaps exist that hinder the implementation of data science: (i) unification of data and (ii) data storage, accessibility, and exchange . This surge of activity in the area of data science was identified and highlighted as a priority research direction (PRD #5) in the “Basic Research Needs for Catalysis Science to Transform Energy Technologies” workshop, where the following key questions were formulated: “How do we augment hypothesis-based catalyst discovery with data science tools, including machine and deep learning, to extract new knowledge from highly diverse datasets?…”

Section: Lessons Learned and Future Opportunitiesmentioning

confidence: 99%

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Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

et al. 2020

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Sustainable energy generation calls for a shift away from centralized, high-temperature, energy-intensive processes to decentralized, low-temperature conversions that can be powered by electricity produced from renewable sources. Electrocatalytic conversion of biomass-derived feedstocks would allow carbon recycling of distributed, energy-poor resources in the absence of sinks and sources of high-grade heat. Selective, efficient electrocatalysts that operate at low temperatures are needed for electrocatalytic hydrogenation (ECH) to upgrade the feedstocks. For effective generation of energy-dense chemicals and fuels, two design criteria must be met: (i) a high H:C ratio via ECH to allow for high-quality fuels and blends and (ii) a lower O:C ratio in the target molecules via electrochemical decarboxylation/deoxygenation to improve the stability of fuels and chemicals. The goal of this review is to determine whether the following questions have been sufficiently answered in the open literature, and if not, what additional information is required: What organic functionalities are accessible for electrocatalytic hydrogenation under a set of reaction conditions? How do substitutions and functionalities impact the activity and selectivity of ECH? What material properties cause an electrocatalyst to be active for ECH? Can general trends in ECH be formulated based on the type of electrocatalyst? What are the impacts of reaction conditions (electrolyte concentration, pH, operating potential) and reactor types?

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

Chen

Tian

et al. 2022

AIChE Journal

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A neural network kinetic model is developed for oxidative coupling of methane (OCM). The model is designed in cognizance of the underlying chemistry and associated reactor balance equations and trained on publicly available high throughput experimental data spanning a large material space of supported mixed metal oxide catalysts. The resultant model is then used to evaluate one of the most popular catalysts for OCM, viz. MnNa2WO4/SiO2, to understand the reaction kinetics and sensitivity of the catalyst to changing different components of the catalyst. The predicted activation barrier for methane conversion is 251 kJ mol−1, and the rate r∼CH40.7O20.6. Furthermore, the reference catalyst is local optimal as small changes to its composition, for example, by changing the individual metals or the support, did not improve (or often substantially reduced) methane consumption or the C2 formation rate.

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Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

Cited by 11 publications

References 74 publications

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

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

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