1
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Behr AS, Borgelt H, Kockmann N. Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management. J Cheminform 2024; 16:16. [PMID: 38326906 PMCID: PMC10851519 DOI: 10.1186/s13321-024-00807-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/22/2024] [Indexed: 02/09/2024] Open
Abstract
As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat .
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Affiliation(s)
- Alexander S Behr
- Laboratory of Equipment Design, Faculty of Biochemical and Chemical Engineering, TU-Dortmund University, Emil-Figge-Strasse 68, 44139, Dortmund, NRW, Germany.
| | - Hendrik Borgelt
- Laboratory of Equipment Design, Faculty of Biochemical and Chemical Engineering, TU-Dortmund University, Emil-Figge-Strasse 68, 44139, Dortmund, NRW, Germany
| | - Norbert Kockmann
- Laboratory of Equipment Design, Faculty of Biochemical and Chemical Engineering, TU-Dortmund University, Emil-Figge-Strasse 68, 44139, Dortmund, NRW, Germany
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2
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Suvarna M, Vaucher AC, Mitchell S, Laino T, Pérez-Ramírez J. Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis. Nat Commun 2023; 14:7964. [PMID: 38042926 PMCID: PMC10693572 DOI: 10.1038/s41467-023-43836-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 11/22/2023] [Indexed: 12/04/2023] Open
Abstract
Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model's adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.
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Affiliation(s)
- Manu Suvarna
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
| | | | - Sharon Mitchell
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland
| | - Teodoro Laino
- IBM Research Europe, Säumerstrasse 4, 8803, Rüschlikon, Switzerland.
| | - Javier Pérez-Ramírez
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.
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3
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Wang G, Mine S, Chen D, Jing Y, Ting KW, Yamaguchi T, Takao M, Maeno Z, Takigawa I, Matsushita K, Shimizu KI, Toyao T. Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach. Nat Commun 2023; 14:5861. [PMID: 37735169 PMCID: PMC10514199 DOI: 10.1038/s41467-023-41341-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/28/2023] [Indexed: 09/23/2023] Open
Abstract
Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.
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Affiliation(s)
- Gang Wang
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Shinya Mine
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Duotian Chen
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Yuan Jing
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Kah Wei Ting
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Taichi Yamaguchi
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Motoshi Takao
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan
| | - Zen Maeno
- School of Advanced Engineering, Kogakuin University, 2665-1, Nakano-cho, Hachioji, 192-0015, Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan.
- Institute for Liberal Arts and Sciences, Kyoto University, 69-302, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8315, Japan.
| | - Koichi Matsushita
- Central Technical Research Laboratory, ENEOS Corporation, 8, Chidori-cho, Naka-ku, Yokohama, 231-0815, Japan
| | - Ken-Ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan.
| | - Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, Sapporo, 001-0021, Japan.
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4
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Giess T, Itzigehl S, Range J, Schömig R, Bruckner JR, Pleiss J. FAIR and scalable management of small-angle X-ray scattering data. J Appl Crystallogr 2023; 56:565-575. [PMID: 37032968 PMCID: PMC10077856 DOI: 10.1107/s1600576723001577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/21/2023] [Indexed: 04/07/2023] Open
Abstract
A modular and extensible research data management toolbox based on the programming language Python and the widely used computing platform Jupyter Notebook has been established for the acquisition, visualization, analysis and storage of small-angle X-ray scattering data. A modular research data management toolbox based on the programming language Python, the widely used computing platform Jupyter Notebook, the standardized data exchange format for analytical data (AnIML) and the generic repository Dataverse has been established and applied to analyze small-angle X-ray scattering (SAXS) data according to the FAIR data principles (findable, accessible, interoperable and reusable). The SAS-tools library is a community-driven effort to develop tools for data acquisition, analysis, visualization and publishing of SAXS data. Metadata from the experiment and the results of data analysis are stored as an AnIML document using the novel Python-native pyAnIML API. The AnIML document, measured raw data and plots resulting from the analysis are combined into an archive in OMEX format and uploaded to Dataverse using the novel easyDataverse API, which makes each data set accessible via a unique DOI and searchable via a structured metadata block. SAS-tools is applied to study the effects of alkyl chain length and counterions on the phase diagrams of alkyltrimethylammonium surfactants in order to demonstrate the feasibility and usefulness of a scalable data management workflow for experiments in physical chemistry.
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Affiliation(s)
- Torsten Giess
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Selina Itzigehl
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Richard Schömig
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Johanna R. Bruckner
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
- Correspondence e-mail:
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5
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Lauterbach S, Dienhart H, Range J, Malzacher S, Spöring JD, Rother D, Pinto MF, Martins P, Lagerman CE, Bommarius AS, Høst AV, Woodley JM, Ngubane S, Kudanga T, Bergmann FT, Rohwer JM, Iglezakis D, Weidemann A, Wittig U, Kettner C, Swainston N, Schnell S, Pleiss J. EnzymeML: seamless data flow and modeling of enzymatic data. Nat Methods 2023; 20:400-402. [PMID: 36759590 DOI: 10.1038/s41592-022-01763-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 12/21/2022] [Indexed: 02/11/2023]
Abstract
The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .
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Affiliation(s)
- Simone Lauterbach
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Hannah Dienhart
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Stephan Malzacher
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Jan-Dirk Spöring
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Dörte Rother
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Maria Filipa Pinto
- i3S, Instituto de Investigação e Inovação em Saúde da Universidade do Porto, University of Porto, Porto, Portugal
| | - Pedro Martins
- i3S, Instituto de Investigação e Inovação em Saúde da Universidade do Porto, University of Porto, Porto, Portugal
| | - Colton E Lagerman
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Andreas S Bommarius
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Amalie Vang Høst
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs Lyngby, Denmark
| | - John M Woodley
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs Lyngby, Denmark
| | - Sandile Ngubane
- Department of Biotechnology and Food Science, Durban University of Technology, Durban, South Africa
| | - Tukayi Kudanga
- Department of Biotechnology and Food Science, Durban University of Technology, Durban, South Africa
| | | | - Johann M Rohwer
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
| | - Dorothea Iglezakis
- Information and Communication Center, University of Stuttgart, Stuttgart, Germany
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | | | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Santiago Schnell
- Department of Biological Sciences and Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany.
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6
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Taniike T, Takahashi K. The value of negative results in data-driven catalysis research. Nat Catal 2023; 6:108-111. [DOI: 10.1038/s41929-023-00920-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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7
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Takahashi K, Ohyama J, Nishimura S, Fujima J, Takahashi L, Uno T, Taniike T. Catalysts informatics: paradigm shift towards data-driven catalyst design. Chem Commun (Camb) 2023; 59:2222-2238. [PMID: 36723221 DOI: 10.1039/d2cc05938j] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Junya Ohyama
- Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, 860-8555, Japan
| | - Shun Nishimura
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Jun Fujima
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
| | - Takeaki Uno
- National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Japan
| | - Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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8
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Tran R, Lan J, Shuaibi M, Wood BM, Goyal S, Das A, Heras-Domingo J, Kolluru A, Rizvi A, Shoghi N, Sriram A, Therrien F, Abed J, Voznyy O, Sargent EH, Ulissi Z, Zitnick CL. The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts. ACS Catal 2023. [DOI: 10.1021/acscatal.2c05426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Affiliation(s)
- Richard Tran
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Janice Lan
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Muhammed Shuaibi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Brandon M. Wood
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Siddharth Goyal
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Abhishek Das
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Javier Heras-Domingo
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Adeesh Kolluru
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Ammar Rizvi
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Nima Shoghi
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Anuroop Sriram
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
| | - Félix Therrien
- Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Scarborough, Ontario M1C 1A4, Canada
| | - Jehad Abed
- Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
- Department of Materials Science and Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
| | - Oleksandr Voznyy
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Scarborough, Ontario M1C 1A4, Canada
| | - Edward H. Sargent
- Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
| | - Zachary Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
- Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - C. Lawrence Zitnick
- Fundamental AI Research, Meta AI, Menlo Park, California 94025, United States
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9
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Salazar A, Wentzel B, Schimmler S, Gläser R, Hanf S, Schunk SA. How Research Data Management Plans Can Help in Harmonizing Open Science and Approaches in the Digital Economy. Chemistry 2023; 29:e202202720. [PMID: 36581496 PMCID: PMC10108121 DOI: 10.1002/chem.202202720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Indexed: 12/31/2022]
Abstract
Within this perspective article, we intend to summarise definitions and terms that are often used in the context of open science and data-driven R&D and we discuss upcoming European regulations concerning data, data sharing and handling. With this background in hand, we take a closer look at the potential connections and permeable interfaces of open science and digital economy, in which data and resulting immaterial goods can become vital pieces as tradeable items. We believe that both science and the digital economy can profit from a seamless transition and foresee that the scientific outcomes of publicly funded research can be better exploited. To close the gap between open science and the digital economy, and to serve for a balancing of the interests of data producers, data consumers, and an economy around services and the public, we introduce the concept of generic research data management plans (RDMs), which have in part been developed through a community effort and which have been evaluated by academic and industry members of the NFDI4Cat consortium. We are of the opinion that in data-driven research, RDMs do need to become a vital element in publicly funded projects.
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Affiliation(s)
- Abel Salazar
- Institute of Chemical Technology, Universität Leipzig, Linnéstr. 3, 04103, Leipzig, Germany
| | - Bianca Wentzel
- Frauenhofer Fokus, Weizenbaum-Institute, Hardenbergstraße 32, 10623, Berlin, Germany
| | - Sonja Schimmler
- Frauenhofer Fokus, Weizenbaum-Institute, Hardenbergstraße 32, 10623, Berlin, Germany
| | - Roger Gläser
- Institute of Chemical Technology, Universität Leipzig, Linnéstr. 3, 04103, Leipzig, Germany
| | - Schirin Hanf
- Institute Inorganic Chemistry, Karlsruhe Institute of Technology (KIT), Engesserstraße 15, 76131, Karlsruhe, Germany
| | - Stephan A Schunk
- Institute of Chemical Technology, Universität Leipzig, Linnéstr. 3, 04103, Leipzig, Germany.,hte GmbH, Kurpfalzring 104, 69123, Heidelberg, Germany.,BASF SE, Carl-Bosch-Str. 38, 67056, Ludwigshafen, Germany
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10
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Korel L, Yorsh U, Behr AS, Kockmann N, Holeňa M. Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis. Computers 2023; 12:14. [DOI: 10.3390/computers12010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing the same domain, different ontologies might be created by different domain experts. To enhance the reasoning and data handling of concepts in scientific papers, finding the best fitting ontology regarding description of the concepts contained in a text corpus. The approach presented in this work attempts to solve this by selection of a representative text paragraph from a set of scientific papers, which are used as data set. Then, using a pre-trained and fine-tuned Transformer, the paragraph is embedded into a vector space. Finally, the embedded vector becomes classified with respect to its relevance regarding a selected target ontology. To construct representative embeddings, we experiment with different training pipelines for natural language processing models. Those embeddings in turn are later used in the task of matching text to ontology. Finally, the result is assessed by compressing and visualizing the latent space and exploring the mappings between text fragments from a database and the set of chosen ontologies. To confirm the differences in behavior of the proposed ontology mapper models, we test five statistical hypotheses about their relative performance on ontology classification. To categorize the output from the Transformer, different classifiers are considered. These classifiers are, in detail, the Support Vector Machine (SVM), k-Nearest Neighbor, Gaussian Process, Random Forest, and Multilayer Perceptron. Application of these classifiers in a domain of scientific texts concerning catalysis research and respective ontologies, the suitability of the classifiers is evaluated, where the best result was achieved by the SVM classifier.
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11
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Yang RX, McCandler CA, Andriuc O, Siron M, Woods-Robinson R, Horton MK, Persson KA. Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis. ACS Nano 2022; 16:19873-19891. [PMID: 36378904 PMCID: PMC9798871 DOI: 10.1021/acsnano.2c08411] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/08/2022] [Indexed: 05/30/2023]
Abstract
The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.
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Affiliation(s)
- Ruo Xi Yang
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
| | - Caitlin A. McCandler
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
| | - Oxana Andriuc
- Department
of Chemistry, University of California, Berkeley, California94720, United States
- Liquid
Sunlight Alliance and Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States
| | - Martin Siron
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
| | - Rachel Woods-Robinson
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
| | - Matthew K. Horton
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California94720, United States
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
| | - Kristin A. Persson
- Department
of Materials Science and Engineering, University
of California, Berkeley, California94720, United States
- Molecular
Foundry, Energy Sciences Area, Lawrence
Berkeley National Laboratory, Berkeley, California94720, United States
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12
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Menke MJ, Behr AS, Rosenthal K, Linke D, Kockmann N, Bornscheuer UT, Dörr M. Development of an Ontology for Biocatalysis. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202200066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Marian J. Menke
- University of Greifswald Dept. of Biotechnology & Enzyme Catalysis Felix-Hausdorff-Strasse 4 17487 Greifswald Germany
| | - Alexander S. Behr
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Strasse 68 44227 Dortmund Germany
| | - Katrin Rosenthal
- TU Dortmund University Department of Biochemical and Chemical Engineering Chair for Bioprocess Engineering Emil-Figge-Strasse 66 44227 Dortmund Germany
| | - David Linke
- Leibniz-Institut für Katalyse e. V. Albert-Einstein-Strasse 29A 18059 Rostock Germany
| | - Norbert Kockmann
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Strasse 68 44227 Dortmund Germany
| | - Uwe T. Bornscheuer
- University of Greifswald Dept. of Biotechnology & Enzyme Catalysis Felix-Hausdorff-Strasse 4 17487 Greifswald Germany
| | - Mark Dörr
- University of Greifswald Dept. of Biotechnology & Enzyme Catalysis Felix-Hausdorff-Strasse 4 17487 Greifswald Germany
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13
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Gossler H, Riedel J, Daymo E, Chacko R, Angeli S, Deutschmann O. A New Approach to Research Data Management with a Focus on Traceability: Adacta. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202200064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Hendrik Gossler
- omegadot software & consulting GmbH Mühlweg 40 67117 Limburgerhof Germany
| | - Johannes Riedel
- omegadot software & consulting GmbH Mühlweg 40 67117 Limburgerhof Germany
- Karlsruhe Institute of Technology Institute for Chemical Technology and Polymer Chemistry Engesserstraße 20 76131 Karlsruhe Germany
| | - Eric Daymo
- Tonkomo LLC 85297 Gilbert Arizona United States
| | - Rinu Chacko
- Karlsruhe Institute of Technology Institute for Chemical Technology and Polymer Chemistry Engesserstraße 20 76131 Karlsruhe Germany
| | - Sofia Angeli
- Karlsruhe Institute of Technology Institute for Chemical Technology and Polymer Chemistry Engesserstraße 20 76131 Karlsruhe Germany
| | - Olaf Deutschmann
- Karlsruhe Institute of Technology Institute for Chemical Technology and Polymer Chemistry Engesserstraße 20 76131 Karlsruhe Germany
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14
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Ziegenbalg D, Pannwitz A, Rau S, Dietzek‐Ivanšić B, Streb C. Comparative Evaluation of Light‐Driven Catalysis: A Framework for Standardized Reporting of Data**. Angew Chem Int Ed Engl 2022; 61:e202114106. [PMID: 35698245 PMCID: PMC9401044 DOI: 10.1002/anie.202114106] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Indexed: 01/05/2023]
Affiliation(s)
- Dirk Ziegenbalg
- Institute of Chemical Engineering Ulm University Albert-Einstein-Allee 11 89081 Ulm Germany
| | - Andrea Pannwitz
- Institute of Inorganic Chemistry I Ulm University Albert-Einstein-Allee 11 89081 Ulm Germany
| | - Sven Rau
- Institute of Inorganic Chemistry I Ulm University Albert-Einstein-Allee 11 89081 Ulm Germany
| | - Benjamin Dietzek‐Ivanšić
- Institute of Physical Chemistry and Center of Energy and Environmental Chemistry Jena (CEEC Jena) Friedrich Schiller University Jena Helmholtzweg 4 07743 Jena Germany
- Department Functional Interfaces Leibniz Institute of Photonic Technology Jena (IPHT) Albert-Einstein-Straße 9 07745 Jena Germany
| | - Carsten Streb
- Institute of Inorganic Chemistry I Ulm University Albert-Einstein-Allee 11 89081 Ulm Germany
- Department of Chemistry Johannes Gutenberg University Mainz Duesbergweg 10-14 55128 Mainz Germany
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15
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Markaj A, Fay A, Kockmann N. Definition, Characterization, and Modeling of Hybrid Modular‐Monolithic Process Plants. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202200048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Artan Markaj
- Helmut Schmidt University Hamburg Department of Mechanical and Civil Engineering Institute of Automation Technology Holstenhofweg 85 22043 Hamburg Germany
| | - Alexander Fay
- Helmut Schmidt University Hamburg Department of Mechanical and Civil Engineering Institute of Automation Technology Holstenhofweg 85 22043 Hamburg Germany
| | - Norbert Kockmann
- TU Dortmund University Department of Biochemical and Chemical Engineering Laboratory of Equipment Design Emil-Figge-Straße 68 44227 Dortmund Germany
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16
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Ziegenbalg D, Pannwitz A, Rau S, Dietzek‐Ivanšić B, Streb C. Vergleichende Evaluierung lichtgetriebener Katalyse: Ein Rahmenkonzept für das standardisierte Berichten von Daten**. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202114106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Dirk Ziegenbalg
- Institut für Chemieingenieurwesen Universität Ulm Albert-Einstein-Allee 11 89081 Ulm Deutschland
| | - Andrea Pannwitz
- Institut für Anorganische Chemie I Universität Ulm Albert-Einstein-Allee 11 89081 Ulm Deutschland
| | - Sven Rau
- Institut für Anorganische Chemie I Universität Ulm Albert-Einstein-Allee 11 89081 Ulm Deutschland
| | - Benjamin Dietzek‐Ivanšić
- Institut für Physikalische Chemie und Center of Energy and Environmental Chemistry Jena (CEEC Jena) Friedrich-Schiller-Universität Jena Helmholtzweg 4 07743 Jena Deutschland
- Department Funktionale Grenzflächen Leibniz-Institut für Photonische Technologien Jena (IPHT) Albert-Einstein-Straße 9 07745 Jena Deutschland
| | - Carsten Streb
- Institut für Anorganische Chemie I Universität Ulm Albert-Einstein-Allee 11 89081 Ulm Deutschland
- Department of Chemistry Johannes Gutenberg University Mainz Duesbergweg 10-14 55128 Mainz Germany
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17
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Jaster T, Gawel A, Siegmund D, Holzmann J, Lohmann H, Klemm E, Apfel UP. Electrochemical CO 2 reduction toward multicarbon alcohols - The microscopic world of catalysts & process conditions. iScience 2022; 25:104010. [PMID: 35345454 PMCID: PMC8956800 DOI: 10.1016/j.isci.2022.104010] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Tackling climate change is one of the undoubtedly most important challenges at the present time. This review deals mainly with the chemical aspects of the current status for converting the greenhouse gas CO2 via electrochemical CO2 reduction reaction (CO2RR) to multicarbon alcohols as valuable products. Feasible reaction routes are presented, as well as catalyst synthesis methods such as electrodeposition, precipitation, or sputtering. In addition, a comprehensive overview of the currently achievable selectivities for multicarbon alcohols in CO2RR is given. It is also outlined to what extent, for example, modifications of the catalyst surfaces or the use of bifunctional compounds the product distribution is shifted. In addition, the influence of varying electrolyte, temperature, and pressure is described and discussed.
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Affiliation(s)
- Theresa Jaster
- Department of Energy, Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, Osterfelder Str. 3, D46047 Oberhausen, Germany
- Inorganic Chemistry I, Ruhr University Bochum, Universitätsstr. 150, D44801 Bochum, Germany
| | - Alina Gawel
- Department of Energy, Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, Osterfelder Str. 3, D46047 Oberhausen, Germany
- Inorganic Chemistry I, Ruhr University Bochum, Universitätsstr. 150, D44801 Bochum, Germany
| | - Daniel Siegmund
- Department of Energy, Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, Osterfelder Str. 3, D46047 Oberhausen, Germany
| | - Johannes Holzmann
- Institute of Chemical Technology, University of Stuttgart, Pfaffenwaldring 55, D70569 Stuttgart, Germany
| | - Heiko Lohmann
- Department of Energy, Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, Osterfelder Str. 3, D46047 Oberhausen, Germany
| | - Elias Klemm
- Institute of Chemical Technology, University of Stuttgart, Pfaffenwaldring 55, D70569 Stuttgart, Germany
| | - Ulf-Peter Apfel
- Department of Energy, Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, Osterfelder Str. 3, D46047 Oberhausen, Germany
- Inorganic Chemistry I, Ruhr University Bochum, Universitätsstr. 150, D44801 Bochum, Germany
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18
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Jablonka KM, Patiny L, Smit B. Making the collective knowledge of chemistry open and machine actionable. Nat Chem 2022; 14:365-376. [PMID: 35379967 DOI: 10.1038/s41557-022-00910-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 02/10/2022] [Indexed: 11/09/2022]
Abstract
Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.
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Affiliation(s)
- Kevin Maik Jablonka
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland
| | - Luc Patiny
- Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland.
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19
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Strömert P, Hunold J, Castro A, Neumann S, Koepler O. Ontologies4Chem: the landscape of ontologies in chemistry. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2021-2007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.
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Affiliation(s)
- Philip Strömert
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
| | - Johannes Hunold
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
| | - André Castro
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry , Weinberg 3 , 06120 Halle , Germany
| | - Oliver Koepler
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
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20
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Chiarelli A, Beagrie N, Boon L, Mallalieu R, Johnson R, May AW, Wilson R. To protect and to serve: developing a road map for research data management services. Insights the UKSG journal 2022. [DOI: 10.1629/uksg.566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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21
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Weber S, Zimmermann RT, Bremer J, Abel KL, Poppitz D, Prinz N, Ilsemann J, Wendholt S, Yang Q, Pashminehazar R, Monaco F, Cloetens P, Huang X, Kübel C, Kondratenko E, Bauer M, Bäumer M, Zobel M, Gläser R, Sundmacher K, Sheppard TL. Digitization in Catalysis Research: Towards a Holistic Description of a Ni/Al2O3 Reference Catalyst for CO2 Methanation. ChemCatChem 2022. [DOI: 10.1002/cctc.202101878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Sebastian Weber
- Karlsruhe Institute of Technology: Karlsruher Institut fur Technologie Institute of Catalysis Research and Technology GERMANY
| | - Ronny T. Zimmermann
- Otto-von-Guericke-University Magdeburg: Otto von Guericke Universitat Magdeburg Institute of Process Engineering GERMANY
| | - Jens Bremer
- Max Planck Institute for Dynamics of Complex Technical Systems: Max-Planck-Institut fur Dynamik komplexer technischer Systeme Department of Process Systems Engineering GERMANY
| | - Ken L. Abel
- Leipzig University: Universitat Leipzig Institute of Chemical Technology GERMANY
| | - David Poppitz
- Leipzig University: Universitat Leipzig Institute of Chemical Technology GERMANY
| | - Nils Prinz
- RWTH Aachen University: Rheinisch-Westfalische Technische Hochschule Aachen Institute of Crystallography GERMANY
| | - Jan Ilsemann
- University of Bremen: Universitat Bremen Institute of Applied and Physical Chemistry GERMANY
| | - Sven Wendholt
- Paderborn University: Universitat Paderborn Faculty of Science and Center for Sustainable Systems Design GERMANY
| | - Qingxin Yang
- Leibniz Institute for Catalysis: Leibniz-Institut fur Katalyse eV LIKAT GERMANY
| | - Reihaneh Pashminehazar
- Karlsruhe Institute of Technology: Karlsruher Institut fur Technologie Institute for Chemical Technology and Polymer Chemistry GERMANY
| | | | - Peter Cloetens
- European Synchrotron Radiation Facility: ESRF ESRF FRANCE
| | - Xiaohui Huang
- Karlsruhe Institute of Technology: Karlsruher Institut fur Technologie Institute of Nanotechnology GERMANY
| | - Christian Kübel
- Karlsruhe Institute of Technology: Karlsruher Institut fur Technologie Institute of Nanotechnology GERMANY
| | - Evgenii Kondratenko
- Leibniz Institute for Catalysis: Leibniz-Institut fur Katalyse eV LIKAT GERMANY
| | - Matthias Bauer
- Paderborn University: Universitat Paderborn Faculty of Science and Center for Sustainable Systems Design GERMANY
| | - Marcus Bäumer
- University of Bremen: Universitat Bremen Institute of Applied and Physical Chemistry GERMANY
| | - Mirijam Zobel
- RWTH Aachen University: Rheinisch-Westfalische Technische Hochschule Aachen Institute of Crystallography GERMANY
| | - Roger Gläser
- Leipzig University: Universitat Leipzig Institute of Chemical Technology GERMANY
| | - Kai Sundmacher
- Otto-von-Guericke-University Magdeburg: Otto von Guericke Universitat Magdeburg Institute of Process Engineering GERMANY
| | - Thomas Lennon Sheppard
- Karlsruher Institut fur Technologie Institute for Chemical Technology and Polymer Chemistry Engesserstrasse 20 76131 Karlsruhe GERMANY
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22
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Mine S, Jing Y, Mukaiyama T, Takao M, Maeno Z, Shimizu KI, Takigawa I, Toyao T. Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction Toward Extrapolative Prediction of Novel Catalysts. CHEM LETT 2022. [DOI: 10.1246/cl.210645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Shinya Mine
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Yuan Jing
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Takumi Mukaiyama
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Motoshi Takao
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Zen Maeno
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
| | - Ichigaku Takigawa
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, N-21, W-10, Sapporo 001-0021, Japan
| | - Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Kyoto 615-8520, Japan
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23
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Mine S, Toyao T, Hinuma Y, Shimizu KI. Understanding and controlling the formation of surface anion vacancies for catalytic applications. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00014h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Systematic computational efforts aimed at calculating surface anion vacancy formation energies as important descriptors of catalytic performance are summarized.
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Affiliation(s)
- Shinya Mine
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
| | - Takashi Toyao
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Nishigyo, Kyoto 615-8520, Japan
| | - Yoyo Hinuma
- Department of Energy and Environment, National Institute of Advanced Industrial Science and Technology (AIST), 1-8-31, Midorigaoka, Ikeda 563-8577, Japan
| | - Ken-ichi Shimizu
- Institute for Catalysis, Hokkaido University, N-21, W-10, 1-5, Sapporo 001-0021, Japan
- Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Katsura, Nishigyo, Kyoto 615-8520, Japan
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24
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Trunschke A. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective. Catal Sci Technol 2022. [DOI: 10.1039/d2cy00275b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.
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Affiliation(s)
- Annette Trunschke
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Department of Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
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25
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Range J, Halupczok C, Lohmann J, Swainston N, Kettner C, Bergmann FT, Weidemann A, Wittig U, Schnell S, Pleiss J. EnzymeML-a data exchange format for biocatalysis and enzymology. FEBS J 2021; 289:5864-5874. [PMID: 34890097 DOI: 10.1111/febs.16318] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/15/2021] [Accepted: 12/09/2021] [Indexed: 11/30/2022]
Abstract
EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.
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Affiliation(s)
- Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Colin Halupczok
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Jens Lohmann
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | | | | | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies, Germany
| | - Santiago Schnell
- Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
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26
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Höving S, Bobers J, Kockmann N. Open-source multi-purpose sensor for measurements in continuous capillary flow. J Flow Chem. [DOI: 10.1007/s41981-021-00214-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Abstract
Limited applicability and scarce availability of analytical equipment for micro- and millifluidic applications, which are of high interest in research and development, complicate process development, control, and monitoring. The low-cost sensor presented in this work is a modular, fast, non-invasive, multi-purpose, and easy to apply solution for detecting phase changes and concentrations of optically absorbing substances in single and multi-phase capillary flow. It aims at generating deeper insight into existing processes in fields of (bio-)chemical and reaction engineering. The scope of this work includes the application of the sensor to residence time measurements in a heat exchanger, a tubular reactor for concentration measurements, a tubular crystallizer for suspension detection, and a pipetting robot for flow automation purposes. In all presented applications either the level of automation has been increased or more information on the investigated system has been gained. Further applications are explained to be realized in the near future.
Article highlights
• An affordable multipurpose sensor for phase differentiation, concentration measurements, and process automation has been developed and characterized
• The sensor is easily modified and can be applied to various tubular reaction/process units for analytical and automation purposes
• Simple integration into existing process control systems is possible
Graphical abstract
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