Catalysis-Hub.org, an open electronic structure database for surface reactions

Winther, Kirsten T.; Hoffmann, Max J.; Boes, Jacob R.; Mamun, Osman; Bajdich, Michal; Bligaard, Thomas

doi:10.1038/s41597-019-0081-y

Cited by 215 publications

(149 citation statements)

References 51 publications

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“…New trends in the methods of storing catalysis data beyond publications in classical journals will also contribute to a change in the paradigm and will help to improve the public access to standardized data, see for example the advancements for computationally generated data in material sciences [123], surface reactions [124], but also experimental data [125].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Towards Experimental Handbooks in Catalysis

et al. 2020

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“…Next, developing a data set that is unbiased, large and noiseless is critical in improving the model accuracy. A great starting point is to use one of the many open databases: ChEMBL, [105] GDB-13, [106] GDB-17, [107] QM9, [108] and ZINC [109] for molecules; Atomic-FLOW for materials discovery (AFLOW), [110] International Crystal Structure Database (ICSD), [111,112] Materials Project (MP), [113] NOMAD, [114] Open Quantum Materials Database (OQMD), [115] Pearson's Crystal Data for crystals, and Catalysishub.org [116] for catalysis. Usually, these open databases are of high quality and suitable for machine learning.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation

Choi

Lee

et al. 2020

Advanced Materials

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The chemical conversion of small molecules such as H2, H2O, O2, N2, CO2, and CH4 to energy and chemicals is critical for a sustainable energy future. However, the high chemical stability of these molecules poses grand challenges to the practical implementation of these processes. In this regard, computational approaches such as density functional theory, microkinetic modeling, data science, and machine learning have guided the rational design of catalysts by elucidating mechanistic insights, identifying active sites, and predicting catalytic activity. Here, the theory and methodologies for heterogeneous catalysis and their applications for small‐molecule activation are reviewed. An overview of fundamental theory and key computational methods for designing catalysts, including the emerging data science techniques in particular, is given. Applications of these methods for finding efficient heterogeneous catalysts for the activation of the aforementioned small molecules are then surveyed. Finally, promising directions of the computational catalysis field for further outlooks are discussed, focusing on the challenges and opportunities for new methods.

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“…Looking at other fields, computational chemistry is a reference one for data sharing and so is computational catalysis [51] . In the particular case of computational materials design for catalysis, infrastructure already exists [53] and some databases already enforce storing under a common format, [54] although universal standards for data sharing are still to be created [52] . Following a recent trend in the field, a strong focus is being put on reproducibility as well [55] .…”

Section: Data Sharing In Catalysismentioning

confidence: 99%

Open Data in Catalysis: From Today's Big Picture to the Future of Small Data

et al. 2020

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Open science and data are yet to make a real breakthrough and research policies will have a critical role in it. The history and general context around open data is hence firstly addressed, including how researchers perceive the existing incentives, leading to recommendations on how to foster data sharing. Subsequently, the focus is on catalysis, with a particular emphasis on benchmarking the data sharing practices against other fields and surveying the type of data currently being shared. The current infrastructure, including data repositories, and standards formats is maped. The striking differences among different disciplines are discussed, serving as a basis to propose specific actions to promote data sharing in catalysis. Short‐term initiatives are needed to boost the amount of openly available data, particularly in heterogeneous catalysis, but a high degree of standardization in data formats will be needed to ensure optimal and automated data mining in the long run. Because of its unique, central role in understanding the catalytic action, kinetic catalytic data is of particular interest. As formats and mining tools are dependant on the type of data, kinetic catalytic data is firstly characterized. Guidelines for a standardized sharing format are proposed, taking into account the small, well‐structured nature of this type of data. To maximize the extraction of information, the low volume of kinetic catalytic data will be compensated by incorporating fundamental knowledge into statistics‐based tools. Whencoupled with knowledge generation tools, i. e. kinetic models, new insights at the active site and mechanism levels will be reached in an ever more automated and powerful way.

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Catalysis-Hub.org, an open electronic structure database for surface reactions

Cited by 215 publications

References 51 publications

Towards Experimental Handbooks in Catalysis

Towards Experimental Handbooks in Catalysis

Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation

Open Data in Catalysis: From Today's Big Picture to the Future of Small Data

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