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The 2019 Materials by Design Roadmap

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The 2019 Materials by Design Roadmap Journal of Physics D: Applied Physics TOPICAL REVIEW • OPEN ACCESS The 2019 materials by design roadmap To cite this article: Kirstin Alberi et al 2019 J. Phys. D: Appl. Phys. 52 013001 View the article online for updates and enhancements. This content was downloaded from IP address 141.14.139.44 on 14/12/2018 at 12:25 IOP Journal of Physics D: Applied Physics Journal of Physics D: Applied Physics J. Phys. D: Appl. Phys. J. Phys. D: Appl. Phys. 52 (2019) 013001 (48pp) https://doi.org/10.1088/1361-6463/aad926 52 Topical Review 2019 The 2019 materials by design roadmap © 2018 IOP Publishing Ltd Kirstin Alberi1 , Marco Buongiorno Nardelli2, Andriy Zakutayev1 , JPAPBE Lubos Mitas3, Stefano Curtarolo4,5, Anubhav Jain6, Marco Fornari7 , Nicola Marzari8, Ichiro Takeuchi9, Martin L Green10, Mercouri Kanatzidis11, 12 13 14 1 013001 Mike F Toney , Sergiy Butenko , Bryce Meredig , Stephan Lany , Ursula Kattner15, Albert Davydov15, Eric S Toberer16, Vladan Stevanovic16, Aron Walsh17,18 , Nam-Gyu Park19, Alán Aspuru-Guzik20,21 , K Alberi et al Daniel P Tabor20 , Jenny Nelson22, James Murphy23, Anant Setlur23, John Gregoire24, Hong Li25, Ruijuan Xiao25, Alfred Ludwig26 , Lane W Martin27,28 , Andrew M Rappe29, Su-Huai Wei30 and John Perkins1 1 National Renewable Energy Laboratory, Golden, CO 80401, United States of America Printed in the UK 2 University of North Texas, Denton, TX, United States of America 3 North Carolina State University, Raleigh, NC, United States of America 4 Duke University, Durham, NC, United States of America JPD 5 Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany 6 Energy Storage and Distributed Resources Department, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America 10.1088/1361-6463/aad926 7 Department of Physics, Central Michigan University, Mt. Pleasant, MI 48859, United States of America 8 Theory and Simulation of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, 1361-6463 Switzerland 9 University of Maryland, College Park, MD, United States of America 10 Materials Measurement Science Division, National Institute of Standards and Technology, 1 Gaithersburg, MD, United States of America 11 Northwestern University, Evanston, IL, United States of America 12 SLAC, Menlo Park, CA, United States of America 13 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States of America 14 Citrine Informatics, Redwood City, CA, United States of America 15 Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America 16 Colorado School of Mines, Golden, CO, United States of America 17 Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom 18 Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea 19 School of Chemical Engineering, Sungkyunkwan University (SKKU), Suwon 440-746, Republic of Korea 20 Department of Chemistry and Department of Computer Science, University of Toronto, Canada 21 Vector Institute for Artificial Intelligence, Toronto, Canada 22 Department of Physics and Centre for Plastic Electronics, Imperial College London, London, United Kingdom 23 Functional Materials Laboratory, GE Global Research, Niskayuna, NY, United States of America 24 California Institute of Technology, Pasadena, CA, United States of America 25 Institute of Physics, Chinese Academy of Sciences, Beijing, People’s Republic of China Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. All references in this article to the phrase ‘materials by design’ are to a concept and not to any party’s trade marks, whether registered or unregistered. No association with any third party trade mark is to be implied in any way by references to the concept. 1361-6463/19/013001+48$33.00 1 © 2018 IOP Publishing Ltd Printed in the UK J. Phys. D: Appl. Phys. 52 (2019) 013001 Topical Review 26 MEMS Materials, Institute for Materials, Ruhr-University Bochum, Bochum, Germany 27 Department of Materials Science and Engineering, University of California, Berkeley, CA, United States of America 28 Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America 29 Department of Chemistry, University of Pennsylvania, Philadelphia, PA, United States of America 30 Beijing Computational Science Research Center, Beijing 100193, People’s Republic of China E-mail: [email protected] Received 19 February 2018, revised 25 June 2018 Accepted for publication 9 August 2018 Published 24 October 2018 Abstract Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design. Keywords: density functional theory, materials genome initative, materials design, high-throughput methods, energy applications (Some figures may appear in colour only in the online journal) Contents 1. Introduction 3 2. Data generation beyond standard DFT for high-throughput applications 5 3. Computational infrastructures for data generation 7 4. Verification and validation for electronic- structure databases 10 5. High-throughput (combinatorial) experimental methods for materials design/discovery 12 6. Advanced in situ and synchrotron based methods for materials design/discovery 15 7. Materials informatics 18 8. Computational prediction and experimental realization of new materials 20 9. Predicting synthesizability 22 10. Thermoelectric materials discovery 25 11. Perovskite photovoltaics 27 12. Organic semiconductors: PV and LEDs 29 13. Materials for solid-state lighting applications 31 14. Chemistry materials: catalysts 33 15. Materials for Li-ion batteries 35 16. Multifunctional metallic alloys 37 17. Functional ceramic materials 39 18. Transparent conducting materials 42 References 44 2 J. Phys. D: Appl. Phys. 52 (2019) 013001 Topical Review 1. Introduction Kirstin Alberi1, Marco Buongiorno Nardelli2 and Andriy Zakutayev1 1 National Renewable Energy Laboratory, Golden, CO 80401, United States of America 2 University of North Texas, Denton, TX, United States of America Advances in renewable and sustainable energy technolo- gies critically depend on our ability to design materials with the optimal properties for each individual application. Computational methods have accelerated materials design efforts through rapid and comprehensive prediction of mat- erials stability and properties. A very simplistic metric for assessing the rise of computational materials efforts is the total number of mat erials that have been ‘predicted’ (which does not capture the extent or diversity of the calculated prop- erties). As schematically shown in figure 1(a), the number of theoretically predicted materials in computational materials property databases, including AFLOW, the Open Quantum Materials Database and the Materials Project (104–106), Figure 1. (a) Total number of compounds contained within the Inorganic Crystal Structure Database (ICSD) and computational is now comparable to the number of experimental entries databases. These values do not reflect the extent of the information in crystallographic databases (~ 105). Perhaps even more in each entry. (b) The number of publications returned in from importantly, increased accessibility to the computed proper- a Scopus search using query terms ‘materials design’ and constraining the search to exclude irrelevant results (e.g. furniture, ties has also sped up exper imental research and development textiles, bridges, etc). of new functional materials for a wide range of applications. Acceleration of materials by design research is evidenced the ability to efficiently generate and manage large amounts of by the nearly exponential growth in the number of publica- computational data in open repositories, facilitating access to tions on materials design, shown in figure 1(b), where break- a plethora of calculated properties and functions of millions of throughs were facilitated by the development of user friendly different materials. Computational efforts that go beyond pre- ab initio codes (mid-90s) and automation of these codes to run dicting the thermodynamic
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