Novel-Materials Discovery

Revealing new and novel materials, mechanisms, and insight

The Scheffler group works in condensed-matter theory, materials science, and artificial intelligence. A particular focus is on density-functional theory and many-electron quantum mechanics and on developments of multiscale approaches. The latter, is summarized by the appeal "Get Real!", introducing environmental factors (e. g. partial pressures, deposition rates, and temperature) into ab initio calculations.[1] In recent years, the work is increasingly concerned with data-centric scientific concepts and methods (the 4th paradigm of materials science)[2][3] and the goal that materials-science data must become "Findable and Artificial Intelligence Ready".

1) H.J. Freund, G. Meijer, M. Scheffler, R. Schlögl, and M. Wolf, CO Oxidation as a Prototypical Reaction for Heterogeneous Processes, Angewandte Chemie International Edition 50: 10064 (2011), https://doi.org/10.1002/anie.201101378.
2) C. Draxl, M. Scheffler, Big Data-Driven Materials Science and Its FAIR Data Infrastructure, in Handbook of Materials Modeling, edited by W. Andreoni and S. Yip: Springer International Publishing, pp. 49 (2021); ISBN 978-3-319-44676-9, S2CID 242594698. https://doi.org/10.1007/978-3-319-44677-6_104.
3) T. Hey, S. Tansley, and K. Tolle, The Fourth Paradigm: Data-Intensive Scientific Discovery (2009), Microsoft Research, ISBN 978-0-9825442-0-4.

Team

Name Phone Email
Rayya Douedari 4720 office.nomad@fhi.mpg.de
Anna Lutz 4710 office.nomad@fhi.mpg.de
Dr. Manoj Dey 4806 dey@fhi.mpg.de
Dr. Luca Foppa 4802 foppa@fhi.mpg.de
Dr. James Green 4804 jgreen@fhi.mpg.de
Dr. Parrydeep Kaur Sachdeva 4851 sachdeva@fhi.mpg.de
Dr. Sebastian Kokott 4821 kokott@fhi.mpg.de
Konstantin Lion 4803 lion@fhi.mpg.de
Dr. Hamid Mehdipour   mehdipour@fhi.mpg.de
Dr. Andrey Sobolev 4807  
Juan Zhang 4864 jzhang@fhi.mpg.de
Dr. Min-Ye Zhang 4719 mzhang@fhi.mpg.de

Publications

2026
Foppa, L. and M. Scheffler: Rethinking Catalysis: Interpretable AI and Description of Real-World Conditions via Materials Genes. Faraday Discussions, in press.
Lawes, N., I. Kowalec, S. Mediavilla-Madrigal, K.J. Aggett, L.R. Smith, M. Dearg, T.J.A. Slater, E. McCarthy, H.I. Rivera-Arrieta, M. Scheffler, D.J. Morgan, D.J. Willock, A.M. Beale, A.J. Logsdail, N.F. Dummer, M. Bowker, C.R.A. Catlow, S.H. Taylor and G.J. Hutchings: The Important Role of Alloy–Oxide Interfaces in Controlling Methanol Formation in CO2 Hydrogenation. ACS Catalysis 16 (3), 2209–2221 (2026).
Kowalec, I., H.I. Rivera-Arrieta, Z. Lu, L. Foppa, M. Scheffler, C.R.A. Catlow and A.J. Logsdails: Role of monodentate formate in product selectivity for CO2 hydrogenation on Pd-based alloy catalysts. Faraday Discussions, in press.
2025
Eibl, S., Y. Yao, M. Scheffler, M. Rampp, L.M. Ghiringhelli and T. Purcell: A high-performance and portable implementation of the SISSO method for CPUs and GPUs. Machine Learning: Science and Technology 6 (4), 047001 (2025).
Aggoune, W. and M. Scheffler: Defect-driven switchable polarization in SrTiO3. Physical Review Materials 9 (11), 114601 (2025).
Ganose, A.M., H. Sahasrabuddhe, M. Asta, K. Beck, T. Biswas, A. Bonkowski, J. Bustamante, X. Chen, Y. Chiang, D.C. Chrzan, J. Clary, O.A. Cohen, C. Ertural, M.C. Gallant, J. George, S. Gerits, R.E.A. Goodall, R.D. Guha, G. Hautier, M. Horton, T.J. Inizan, A.D. Kaplan, R.S. Kingsbury, M.C. Kuner, B. Li, X. Linn, M.J. McDermott, R.S. Mohanakrishnan, A.A. Naik, J.B. Neaton, S.M. Parmar, K.A. Persson, G. Petretto, T.A.R. Purcell, F. Ricci, B. Rich, J. Riebesell, G.-M. Rignanese, A.S. Rosen, M. Scheffler, J. Schmidt, J.-X. Shen, A. Sobolev, R. Sundararaman, C. Tezak, V. Trinquet, J.B. Varley, D. Vigil-Fowler, D. Wang, D. Waroquiers, M. Wen, H. Yang, H. Zheng, J. Zheng, Z. Zhu and A. Jain: Correction: Atomate2: modular workflows for materials science. Digital Discovery 4 (9), 2639–2640 (2025).
Foppa, L. and M. Scheffler: Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups. Digital Discovery 4 (8), 2175–2187 (2025).
Mauß, J.M., K.S. Kley, R. Khobragade, N.K. Tran, J. De Bellis, F. Schüth, M. Scheffler and L. Foppa: Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions via Experiments and AI. ACS Catalysis 15 (15), 12652–12665 (2025).
Nair, A.S., L. Foppa and M. Scheffler: Materials Database from All-electron Hybrid Functional DFT Calculations. Scientific Data 12, 1518 (2025).
Liu, C., A. Meledin, W. Aggoune, Y. Sun, M. Abdeldayem, T. Remmele, A. Fiedler, T. Schulz, Y. Yang, J. Schwarzkopf, M. Scheffler, M. Albrecht and D. Zhou: Ex Situ and In Situ STEM Investigations of Memristive Switching Mechanisms in Off-stoichiometric SrTiO3. Microscopy and Microanalysis 31 (Suppl. 1), ozaf048.561 (2025).
Ganose, A.M., H. Sahasrabuddhe, M. Asta, K. Beck, T. Biswas, A. Bonkowski, J. Bustamante, X. Chen, Y. Chiang, D.C. Chrzan, J. Clary, O.A. Cohen, C. Ertural, M.C. Gallant, J. George, S. Gerits, R.E.A. Goodall, R.D. Guha, G. Hautier, M. Horton, T.J. Inizan, A.D. Kaplan, R.S. Kingsbury, M.C. Kuner, B. Li, X. Linn, M.J. McDermott, R.S. Mohanakrishnan, A.N. Naik, J.B. Neaton, S.M. Parmar, K.A. Persson, G. Petretto, T.A.R. Purcell, F. Ricci, B. Rich, J. Riebesell, G.-M. Rignanese, A.S. Rosen, M. Scheffler, J. Schmidt, J.-X. Shen, A. Sobolev, R. Sundararaman, C. Tezak, V. Trinquet, J.B. Varley, D. Vigil-Fowler, D. Wang, D. Waroquiers, M. Wen, H. Yang, H. Zheng, J. Zheng, Z. Zhu and A. Jain: Atomate2: modular workflows for materials science. Digital Discovery 2025 (7), 1944–1973 (2025).
Kokott, S., V. Blum and M. Scheffler: Efficient computation of the long-range exact exchange using an extended screening function. The Journal of Chemical Physics 162 (22), 224103 (2025).
Kang, K., T. Purcell, C. Carbogno and M. Scheffler: Accelerating the training and improving the reliability of machine-learned interatomic potentials for strongly anharmonic materials through active learning. Physical Review Materials 9 (6), 063801 (2025).
Sugathan Nair, A., L. Foppa and M. Scheffler: Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis. npj Computational Materials 11, 150 (2025).
Moerman, E., H. Miranda, A. Gallo, A. Irmler, T. Schäfer, F. Hummel, M. Engel, G. Kresse, M. Scheffler and A. Grüneis: Exploring the accuracy of the equation-of-motion coupled-cluster band gap of solids. Physical Review B 111 (12), L121202 (2025).
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Talks

2026
Scheffler, M.: AI for Materials Discovery: Identifying Materials Genes and Property Landscapes, Quantifying Uncertainties. Gordon Research Conference, AI for Materials, Energy, and Chemical Sciences, Galveston, TX, USA (2026)
Scheffler, M.: Quantifying Trust in Machine-Learning: Application to Catalyst Discovery. Colloquium of the Institute for Catalysis, Hokkaido University, Sapporo, Japan (2026)
Scheffler, M.: The Concept of Materials Genes and the Discovery of Improved Materials. International Symposium on Atomic Level Characterizations for New Materials and Devices in Winter 2026, Furano, Japan (2026)
2025
Scheffler, M.: Modelling Heat and Charge Transport for Strongly Anharmonic Materials. Hands-On Workshop on Electronic-Structure Theory and Artificial Intelligence for Materials Science, Shanghai University, Shanghai, China (2025)
Scheffler, M.: Data-Centric Materials Science and the Search for Materials Genes Governing Properties and Functions. 11th International Petra School of Physics (PSP11), Artificial Intelligence in the Natural Sciences, Amman, Jordan (2025)
Scheffler, M.: Beyond Big Data: Information-Rich Databases and the Active Learning Paradigm. 11th International Petra School of Physics (PSP11), Artificial Intelligence in the Natural Sciences, Irbid, Jordan (2025)
Scheffler, M.: Get Real! Heat and Charge Transport Beyond the Harmonic World. Xingda Lecture/Chemistry Colloquium, College of Chemistry, Peking University, Beijing, China (2025)
Scheffler, M.: AI-Driven Discovery: Symbolic Regression Unveils Acid-Stable Oxides for Catalysis. Symposium, Frontiers in the Chemical Physics of Heterogeneous Interfaces and Beyond, Frick Chemistry Laboratory, Princeton University, Princeton, NJ, USA (2025)
Scheffler, M.: Artificial Intelligence for Materials Science. Physics Colloquium, University of Luxembourg, Luxembourg (2025)
Scheffler, M.: Artificial Intelligence for Materials Science. DPG Spring Meeting of the Condensed Matter Section (SKM) , Regensburg, Germany (2025)
Scheffler, M.: Discovery of Novel Memristor Materials by Artificial Intelligence. AWASES Merck-Intel Workshop, Darmstadt, Germany (2025)
Scheffler, M.: Density Functional Theory and Artificial Intelligence in Materials Science. Seminar, Condensed Matter Theory Group, The University of Sydney, Sydney, Australia (2025)
Scheffler, M.: Artificial Intelligence in Materials Science. Colloquium, School of Physics, The University of Sydney, Sydney, Australia (2025)
2024
Scheffler, M.: Data-Centric Materials Science. 15th International Symposium on Atomic Level Characterizations for New Materials and Devices`24, Matsue, Japan (2024)
Scheffler, M.: Conclusion of the Conference and Outlook for the Field. International Workshop on Data-Driven Computational and Theoretical Materials Design, Shanghai, China (2024)
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