1
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Kraus P, Bainglass E, Ramirez FF, Svaluto-Ferro E, Ercole L, Kunz B, Huber SP, Plainpan N, Marzari N, Battaglia C, Pizzi G. A bridge between trust and control: computational workflows meet automated battery cycling. JOURNAL OF MATERIALS CHEMISTRY. A 2024; 12:10773-10783. [PMID: 38725523 PMCID: PMC11077506 DOI: 10.1039/d3ta06889g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/20/2024] [Indexed: 05/12/2024]
Abstract
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.
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Affiliation(s)
- Peter Kraus
- Materials for Energy Conversion, Empa Überlandstr. 129 8600 Dübendorf Switzerland
- Technische Universität Berlin, Centre for Advanced Ceramic Materials Hardenbergstr. 40 10623 Berlin Germany
| | - Edan Bainglass
- Laboratory for Materials Simulations (LMS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Paul Scherrer Institute 5232 Villigen Switzerland
| | - Francisco F Ramirez
- Theory and Simulations of Materials (THEOS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Enea Svaluto-Ferro
- Materials for Energy Conversion, Empa Überlandstr. 129 8600 Dübendorf Switzerland
| | - Loris Ercole
- Theory and Simulations of Materials (THEOS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Benjamin Kunz
- Materials for Energy Conversion, Empa Überlandstr. 129 8600 Dübendorf Switzerland
| | - Sebastiaan P Huber
- Theory and Simulations of Materials (THEOS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Nukorn Plainpan
- Materials for Energy Conversion, Empa Überlandstr. 129 8600 Dübendorf Switzerland
| | - Nicola Marzari
- Laboratory for Materials Simulations (LMS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Paul Scherrer Institute 5232 Villigen Switzerland
- Theory and Simulations of Materials (THEOS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Corsin Battaglia
- Materials for Energy Conversion, Empa Überlandstr. 129 8600 Dübendorf Switzerland
- ETH Zurich, Department of Information Technology and Electrical Engineering Gloriastrasse 35 8092 Zurich Switzerland
- Institute of Materials (IMX), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
| | - Giovanni Pizzi
- Laboratory for Materials Simulations (LMS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Paul Scherrer Institute 5232 Villigen Switzerland
- Theory and Simulations of Materials (THEOS), National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland
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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] [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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Rüther F, Machado R, Gioria E, Kunz SL, Wittich K, Löser P, Geske M, Schunk SA, Glaum R, Rosowski F. Niobium Insertion into α II-VOPO 4: Tuning the Catalytic Properties for Selective Oxidation. ACS Catal 2023. [DOI: 10.1021/acscatal.2c06209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Affiliation(s)
- Frederik Rüther
- BasCat - UniCat BASF JointLab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Rhea Machado
- BasCat - UniCat BASF JointLab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Esteban Gioria
- BasCat - UniCat BASF JointLab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Sylvia L. Kunz
- Institut für Anorganische Chemie der Rheinischen Friedrich-Wilhelms-Universität Bonn, 53121 Bonn, Germany
| | | | | | - Michael Geske
- BasCat - UniCat BASF JointLab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Stephan A. Schunk
- hte GmbH, 69123 Heidelberg, Germany
- Institute of Chemical Technology, Universität Leipzig, 04103 Leipzig, Germany
| | - Robert Glaum
- Institut für Anorganische Chemie der Rheinischen Friedrich-Wilhelms-Universität Bonn, 53121 Bonn, Germany
| | - Frank Rosowski
- BasCat - UniCat BASF JointLab, Technische Universität Berlin, 10623 Berlin, Germany
- BASF SE, Catalysis Research, 67063 Ludwigshafen, Germany
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4
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Foppa L, Rüther F, Geske M, Koch G, Girgsdies F, Kube P, Carey SJ, Hävecker M, Timpe O, Tarasov AV, Scheffler M, Rosowski F, Schlögl R, Trunschke A. Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation. J Am Chem Soc 2023; 145:3427-3442. [PMID: 36745555 PMCID: PMC9936587 DOI: 10.1021/jacs.2c11117] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called "materials genes" of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N2 adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides "rules" on how the catalyst properties may be tuned in order to achieve the desired performance.
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Affiliation(s)
- Lucas Foppa
- The
NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany,
| | - Frederik Rüther
- BasCat
- UniCat BASF JointLab, Hardenbergstraße 36, D-10623 Berlin, Germany
| | - Michael Geske
- BasCat
- UniCat BASF JointLab, Hardenbergstraße 36, D-10623 Berlin, Germany
| | - Gregor Koch
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Frank Girgsdies
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Pierre Kube
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Spencer J. Carey
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Michael Hävecker
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany,Max
Planck Institute for Chemical Energy Conversion, 45470 Mülheim, Germany
| | - Olaf Timpe
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Andrey V. Tarasov
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Matthias Scheffler
- The
NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Frank Rosowski
- BasCat
- UniCat BASF JointLab, Hardenbergstraße 36, D-10623 Berlin, Germany,BASF
SE, Catalysis Research, Carl-Bosch-Straße 38, D-67065 Ludwigshafen, Germany
| | - Robert Schlögl
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Annette Trunschke
- Department
of Inorganic Chemistry, Fritz-Haber-Institut
of the Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany,
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5
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Exploring catalytic reaction networks with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-022-00896-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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6
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Rüther F, Baumgarten R, Ebert F, Gioria E, Naumann d'Alnoncourt R, Trunschke A, Rosowski F. Tuning catalysis by surface-deposition of elements on oxidation catalysts via atomic layer deposition. Catal Sci Technol 2023. [DOI: 10.1039/d2cy02184f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
This study on surface-modifications of bulk oxidation catalysts with sub-monolayers of POx, BOx and MnOxvia atomic layer deposition demonstrates this method to be a powerful tool for tuning the performance in selective oxidations of light alkanes.
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7
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Bowker M, DeBeer S, Dummer NF, Hutchings GJ, Scheffler M, Schüth F, Taylor SH, Tüysüz H. Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT). Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202209016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Michael Bowker
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
| | - Serena DeBeer
- Max Planck Institute for Chemical Energy Conversion Germany
| | - Nicholas F. Dummer
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
| | - Graham J. Hutchings
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
| | - Matthias Scheffler
- The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS Adlershof of the Humboldt Universität zu Berlin Germany
| | | | - Stuart H. Taylor
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
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8
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Bowker M, DeBeer S, Dummer NF, Hutchings GJ, Scheffler M, Schüth F, Taylor SH, Tüysüz H. Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT). Angew Chem Int Ed Engl 2022; 61:e202209016. [PMID: 36351240 PMCID: PMC10099920 DOI: 10.1002/anie.202209016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Indexed: 11/11/2022]
Abstract
Catalysis is involved in around 85 % of manufacturing industry and contributes an estimated 25 % to the global domestic product, with the majority of the processes relying on heterogeneous catalysis. Despite the importance in different global segments, the fundamental understanding of heterogeneously catalysed processes lags substantially behind that achieved in other fields. The newly established Max Planck-Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT) targets innovative concepts that could contribute to the scientific developments needed in the research field to achieve net zero greenhouse gas emissions in the chemical industries. This Viewpoint Article presents some of our research activities and visions on the current and future challenges of heterogeneous catalysis regarding green industry and the circular economy by focusing explicitly on critical processes. Namely, hydrogen production, ammonia synthesis, and carbon dioxide reduction, along with new aspects of acetylene chemistry.
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Affiliation(s)
- Michael Bowker
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
| | - Serena DeBeer
- Max Planck Institute for Chemical Energy Conversion Germany
| | - Nicholas F. Dummer
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
| | - Graham J. Hutchings
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
| | - Matthias Scheffler
- The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS Adlershof of the Humboldt Universität zu Berlin Germany
| | | | - Stuart H. Taylor
- Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis Cardiff Catalysis Institute School of Chemistry Cardiff University Cardiff CF10 3AT UK
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9
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Chattoraj J, Hamadicharef B, Kong JF, Pargi MK, Zeng Y, Poh CK, Chen L, Gao F, Tan TL. Theory‐guided machine learning to predict the performance of noble metal catalysts in the water‐gas shift reaction. ChemCatChem 2022. [DOI: 10.1002/cctc.202200355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Joyjit Chattoraj
- IHPC: Institute of High Performance Computing Computing & Intelligence 1 Fusionopolis Way#16-16Connexis North Tower 138632 Singapore SINGAPORE
| | - Brahim Hamadicharef
- IHPC: Institute of High Performance Computing Computing and Intelligence SINGAPORE
| | - Jian Feng Kong
- IHPC: Institute of High Performance Computing Materials Science and Chemistry SINGAPORE
| | - Mohan Kashyap Pargi
- IHPC: Institute of High Performance Computing Computing and Intelligence SINGAPORE
| | - Yingzhi Zeng
- IHPC: Institute of High Performance Computing Materials Science and Chemistry SINGAPORE
| | - Chee Kok Poh
- Institute of Sustainability for Chemicals Energy and Environment Catalysis and Reactor Design SINGAPORE
| | - Luwei Chen
- Institute of Sustainability for Chemicals Energy and Environment Catalysis and Reactor Design SINGAPORE
| | - Fei Gao
- IHPC: Institute of High Performance Computing Computing and Intelligence SINGAPORE
| | - Teck Leong Tan
- IHPC: Institute of High Performance Computing Materials Science and Chemistry SINGAPORE
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10
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Quo Vadis Dry Reforming of Methane?—A Review on Its Chemical, Environmental, and Industrial Prospects. Catalysts 2022. [DOI: 10.3390/catal12050465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In recent years, the catalytic dry reforming of methane (DRM) has increasingly come into academic focus. The interesting aspect of this reaction is seemingly the conversion of CO2 and methane, two greenhouse gases, into a valuable synthesis gas (syngas) mixture with an otherwise unachievable but industrially relevant H2/CO ratio of one. In a possible scenario, the chemical conversion of CO2 and CH4 to syngas could be used in consecutive reactions to produce synthetic fuels, with combustion to harness the stored energy. Although the educts of DRM suggest a superior impact of this reaction to mitigate global warming, its potential as a chemical energy converter and greenhouse gas absorber has still to be elucidated. In this review article, we will provide insights into the industrial maturity of this reaction and critically discuss its applicability as a cornerstone in the energy transition. We derive these insights from assessing the current state of research and knowledge on DRM. We conclude that the entire industrial process of syngas production from two greenhouse gases, including heating with current technologies, releases at least 1.23 moles of CO2 per mol of CO2 converted in the catalytic reaction. Furthermore, we show that synthetic fuels derived from this reaction exhibit a negative carbon dioxide capturing efficiency which is similar to burning methane directly in the air. We also outline potential applications and introduce prospective technologies toward a net-zero CO2 strategy based on DRM.
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11
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Scheffler M, Aeschlimann M, Albrecht M, Bereau T, Bungartz HJ, Felser C, Greiner M, Groß A, Koch CT, Kremer K, Nagel WE, Scheidgen M, Wöll C, Draxl C. FAIR data enabling new horizons for materials research. Nature 2022; 604:635-642. [PMID: 35478233 DOI: 10.1038/s41586-022-04501-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 01/28/2022] [Indexed: 12/30/2022]
Abstract
The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.
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Affiliation(s)
- Matthias Scheffler
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany.,The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Berlin, Germany
| | - Martin Aeschlimann
- Department of Physics and Research Center OPTIMAS, University of Kaiserslautern, Kaiserslautern, Germany
| | | | - Tristan Bereau
- Max-Planck-Institut für Polymerforschung, Mainz, Germany
| | | | - Claudia Felser
- Max Planck Institute for Chemical Physics of Solids, Dresden, Germany
| | - Mark Greiner
- Max Planck Institute for Chemical Energy Conversion, Mülheim an der Ruhr, Germany
| | - Axel Groß
- Institute of Theoretical Chemistry, Ulm University and Helmholtz-Institute Ulm, Ulm, Germany
| | - Christoph T Koch
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kurt Kremer
- Max-Planck-Institut für Polymerforschung, Mainz, Germany
| | - Wolfgang E Nagel
- Computer Science Department, Technical University Dresden, Dresden, Germany
| | - Markus Scheidgen
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christof Wöll
- Institute of Functional Interfaces, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Claudia Draxl
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany. .,The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Berlin, Germany.
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12
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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] [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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13
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14
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Knemeyer K, Epping JD, Rüther F, Schulz C, Frank B, Müller P, d'Alnoncourt RN, Driess M, Rosowski F. Unraveling Property‐Performance Relationships by Surface Tailoring of Oxidation Catalysts
via
ALD. ChemCatChem 2021. [DOI: 10.1002/cctc.202100866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Kristian Knemeyer
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstr. 36 10623 Berlin Germany
| | - Jan Dirk Epping
- Institut für Chemie Technische Universität Berlin Str. des 17. Juni 155 10623 Berlin Germany
| | - Frederik Rüther
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstr. 36 10623 Berlin Germany
| | - Christian Schulz
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstr. 36 10623 Berlin Germany
- hte Aktiengesellschaft Kurpfalzring 104 69123 Heidelberg Germany
| | - Benjamin Frank
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstr. 36 10623 Berlin Germany
| | - Philipp Müller
- Process Research and Chemical Engineering BASF SE Carl-Bosch-Straße 38 67056 Ludwigshafen Germany
| | | | - Matthias Driess
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstr. 36 10623 Berlin Germany
- Institut für Chemie Technische Universität Berlin Str. des 17. Juni 155 10623 Berlin Germany
| | - Frank Rosowski
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstr. 36 10623 Berlin Germany
- Process Research and Chemical Engineering BASF SE Carl-Bosch-Straße 38 67056 Ludwigshafen Germany
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15
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Foppa L, Ghiringhelli LM, Girgsdies F, Hashagen M, Kube P, Hävecker M, Carey SJ, Tarasov A, Kraus P, Rosowski F, Schlögl R, Trunschke A, Scheffler M. Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence. MRS BULLETIN 2021; 46:1016-1026. [PMID: 35221466 PMCID: PMC8825435 DOI: 10.1557/s43577-021-00165-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/21/2021] [Indexed: 06/14/2023]
Abstract
ABSTRACT The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data," containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design. IMPACT STATEMENT Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1557/s43577-021-00165-6.
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Affiliation(s)
- Lucas Foppa
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
| | - Luca M. Ghiringhelli
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
| | - Frank Girgsdies
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Maike Hashagen
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Pierre Kube
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Michael Hävecker
- Max-Planck-Institut für Chemische Energiekonversion, Mülheim, Germany
| | - Spencer J. Carey
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Andrey Tarasov
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
| | - Peter Kraus
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
- Present Address: School of Molecular and Life Sciences, Curtin University, Perth, Australia
| | - Frank Rosowski
- BASF SE, Process Reseach and Chemical Engineering, Heterogeneous Catalysis, Ludwigshafen, Germany
| | - Robert Schlögl
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
- Max-Planck-Institut für Chemische Energiekonversion, Mülheim, Germany
| | | | - Matthias Scheffler
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany
- Humboldt-Universität zu Berlin, Berlin, Germany
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16
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Bao X, Behrens M, Ertl G, Fu Q, Knop-Gericke A, Lunkenbein T, Muhler M, Schmidt CM, Trunschke A. A Career in Catalysis: Robert Schlögl. ACS Catal 2021. [DOI: 10.1021/acscatal.1c01165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xinhe Bao
- State Key Laboratory of Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), 457 Zhongshan Road, Dalian 116023, People’s Republic of China
| | - Malte Behrens
- Institute of Inorganic Chemistry, Solid State Chemistry and Catalysis, Kiel University, Max-Eyth-Straße 2, 24118 Kiel, Germany
| | - Gerhard Ertl
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Departments of Physical Chemistry and Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
| | - Qiang Fu
- State Key Laboratory of Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), 457 Zhongshan Road, Dalian 116023, People’s Republic of China
| | - Axel Knop-Gericke
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Departments of Physical Chemistry and Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
- Max-Planck-Institut für Chemische Energiekonversion, Stiftstraße 34-36, 45470 Mülheim, Germany
| | - Thomas Lunkenbein
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Departments of Physical Chemistry and Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
| | - Martin Muhler
- Industrial Chemistry, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, Germany
| | - Christoph M. Schmidt
- RWI - Leibniz-Institut für Wirtschaftsforschung, Hohenzollernstraße 1-3, 45128 Essen, Germany
| | - Annette Trunschke
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Departments of Physical Chemistry and Inorganic Chemistry, Faradayweg 4-6, 14195 Berlin, Germany
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17
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Wulf C, Beller M, Boenisch T, Deutschmann O, Hanf S, Kockmann N, Kraehnert R, Oezaslan M, Palkovits S, Schimmler S, Schunk SA, Wagemann K, Linke D. A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts. ChemCatChem 2021. [DOI: 10.1002/cctc.202001974] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Christoph Wulf
- Leibniz-Institute for Catalysis Rostock Albert-Einstein-Str. 29a D-18059 Rostock Germany
| | - Matthias Beller
- Leibniz-Institute for Catalysis Rostock Albert-Einstein-Str. 29a D-18059 Rostock Germany
| | - Thomas Boenisch
- High Performance Computing Center Stuttgart (HLRS) University of Stuttgart Nobelstr. 19 D-70569 Stuttgart Germany
| | - Olaf Deutschmann
- Karlsruher Institut für Technologie (KIT) Kaiserstraße 12 D-76131 Karlsruhe Germany
| | - Schirin Hanf
- Karlsruher Institut für Technologie (KIT) Engesserstr. 15 D-76131 Karlsruhe Germany
| | - Norbert Kockmann
- Biochemical and Chemical Engineering, Equipment Design TU Dortmund University D-44221 Dortmund Germany
| | - Ralph Kraehnert
- BasCat – UniCat BASF JointLab Technische Universität Berlin Hardenbergstraße 36 D-10623 Berlin Germany
| | - Mehtap Oezaslan
- Institute of Technical Chemistry TU Braunschweig D-38106 Braunschweig Germany
| | - Stefan Palkovits
- Institute for Technical and Macromolecular Chemistry RWTH Aachen University Worringerweg 2 D-52074 Aachen Germany
| | - Sonja Schimmler
- Fraunhofer Institute for Open Communication Systems (FOKUS) Kaiserin-Augusta-Allee 31 D-10589 Berlin Germany
| | - Stephan A. Schunk
- the high throughput experimentation company Kurpfalzring 104 D-69123 Heidelberg Germany
- BASF SE Carl-Bosch Str. 38 D-67056 Ludwigshafen Germany
| | - Kurt Wagemann
- DECHEMA e.V. Theodor-Heuss-Allee 25 D-60486 Frankfurt Germany
| | - David Linke
- Leibniz-Institute for Catalysis Rostock Albert-Einstein-Str. 29a D-18059 Rostock Germany
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