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. Wahib Aggoune 4852 aggoune@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

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).
Moerman, E., A. Gallo, A. Irmler, T. Schäfer, F. Hummel, A. Grüneis and M. Scheffler: Finite-Size Effects in Periodic EOM-CCSD for Ionization Energies and Electron Affinities: Convergence Rate and Extrapolation to the Thermodynamic Limit. Journal of Chemical Theory and Computation 21 (4), 1865–1878 (2025).
Bellini, G., G. Koch, F. Girgsdies, J. Dong, S. Carey, O. Timpe, G. Auffermann, M. Scheffler, R. Schlögl, L. Foppa and A. Trunschke: CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and Artificial Intelligence. Angewandte Chemie International Edition 64 (6), e202417812 (2025).
Speckhard, D., C. Carbogno, L.M. Ghiringhelli, S. Lubeck, M. Scheffler and C. Draxl: Extrapolation to the complete basis-set limit in density-functional theory using statistical learning. Physical Review Materials 9 (1), 013801 (2025).
2024
Miyazaki, R., S. Faraji, S.V. Levchenko, L. Foppa and M. Scheffler: Vibrational frequencies utilized for the assessment of exchange-correlation functionals in the description of metal-adsorbate systems: C2H2 and C2H4 on transition-metal surfaces. Catalysis Science & Technology 14 (23), 6924–6933 (2024).
Quan, J., C. Carbogno and M. Scheffler: Carrier Mobility of Strongly Anharmonic Materials from First Principles. Physical Review B 110 (23), 235202 (2024).
Bauer, S., P. Benner, T. Bereau, V. Blum, M. Boley, C. Carbogno, C.R.A. Catlow, G. Dehm, S. Eibl, R. Ernstorfer, A. Fekete, L. Foppa, P. Fratzl, C. Freysoldt, B. Gault, L.M. Ghiringhelli, S.K. Giri, A. Gladyshev, P. Goyal, J. Hattrick-Simpers, L. Kabalan, P. Karpov, M.S. Khorrami, C. Koch, S. Kokott, T. Kosch, I. Kowalec, K. Kremer, A. Leitherer, Y. Li, C.H. Liebscher, A.J. Logsdail, Z. Lu, F. Luong, A. Marek, F. Merz, J.R. Mianroodi, J. Neugebauer, Z. Pei, T. Purcell, D. Raabe, M. Rampp, M. Rossi, J.-M. Rost, J.E. Saal, U. Saalmann, K.N. Sasidhar, A. Saxena, L. Sbailo, M. Scheidgen, M. Schloz, D.F. Schmidt, S. Teshuva, A. Trunschke, Y. Wei, G. Weikum, R.P. Xian, Y. Yao, J. Yin, M. Zhao and M. Scheffler: Roadmap on data-centric materials science. Modelling and Simulation in Materials Science and Engineering 32 (6), 063301 (2024).
Kokott, S., F. Merz, Y. Yao, C. Carbogno, M. Rossi, V. Havu, M. Rampp, M. Scheffler and V. Blum: Efficient all-electron hybrid density functionals for atomistic simulations beyond 10 000 atoms. The Journal of Chemical Physics 161 (2), 024112 (2024).
Miyazaki, R., K.S. Belthle, H. Tüysüz, L. Foppa and M. Scheffler: Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data. Journal of the American Chemical Society 146 (8), 5433–5444 (2024).
Bi, S., C. Carbogno, I.Y. Zhang and M. Scheffler: Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework. The Journal of Chemical Physics 160 (3), 034106 (2024).
2023
Dean, J., M. Scheffler, T. Purcell, S.V. Barabash, R. Bhowmik and T. Bazhirov: Interpretable Machine Learning for Materials Design. Journal of Materials Research 38 (20), 4477–4496 (2023).
Purcell, T., M. Scheffler and L.M. Ghiringhelli: Recent advances in the SISSO method and their implementation in the SISSO++ Code. The Journal of Chemical Physics 159 (11), 114110 (2023).
Gavini, V., S. Baroni, V. Blum, D.R. Bowler, A. Buccheri, J.R. Chelikowsky, S. Das, W. Dawson, P. Delugas, M. Dogan, C. Draxl, G. Galli, L. Genovese, P. Giannozzi, M. Giantomassi, X. Gonze, M. Govoni, F. Gygi, A. Gulans, J.M. Herbert, S. Kokott, T.D. Kühne, K.-H. Liou, T. Miyazaki, P. Motamarri, A. Nakata, J.E. Pask, C. Plessl, L.E. Ratcliff, R.M. Richard, M. Rossi, R. Schade, M. Scheffler, O. Schütt, P. Suryanarayana, M. Torrent, L. Truflandier, T.L. Windus, Q. Xu, V.W.-Z. Yu and D. Perez: Roadmap on electronic structure codes in the exascale era. Modelling and Simulation in Materials Science and Engineering 31 (6), 063301 (2023).
Langer, M.F., F. Knoop, C. Carbogno, M. Scheffler and M. Rupp: Heat flux for semilocal machine-learning potentials. Physical Review B 108 (10), L100302 (2023).
Purcell, T., M. Scheffler, L.M. Ghiringhelli and C. Carbogno: Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence. npj Computational Materials 9, 112 (2023).
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Talks

2025
Scheffler, M.: Artificial Intelligence for Materials Science. DPG Spring Meeting 2025, Regensburg, Germany (2025)
Scheffler, M.: Discovery of Novel Memristor Materials by Artificial Intelligence. AWASES Merck-Intel Workshop, Merck , 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)
Scheffler, M.: The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society. Colloquium at School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China (2024)
Scheffler, M.: Density Functional Theory and Artificial Intelligence in Materials Science. Meeting at Synfuels China, Beijing, China (2024)
Scheffler, M.: Artificial Intelligence in Materials Science: Impact, Uncertain Expectations, and Open Challenges. Colloquium, School of Physics, Peking University, Beijing, China (2024)
Scheffler, M.: Impact, Uncertain Expectations, and Open Challenges of AI in Materials Science. Conference of Condensed Matter Physics (CCMP), Liyang, Jiangsu, China (2024)
Scheffler, M.: Exascale-Critical Advancements in the FHI-Aims Software. Final NOMAD CoE Review Meeting, European Commission, Online (2024)
Scheffler, M.: Open Data and Artificial Intelligence in Materials Science and Engineering: A New Way of Thinking Research. Annual Magna Meeting, Academia Brasileira de Ciencias, Rio de Janeiro, Brazil (2024)
Scheffler, M.: AI Guided Workflows for Efficiently Screening the Materials Space. Coshare Science 02, Online Event (2024)
Scheffler, M.: AI-Guided Workflows for Efficiently Screening the Materials Space. Physics Colloquium, Universität Duisburg-Essen, Essen, Germany (2024)
Scheffler, M.: Open Science and AI: Künstliche Intelligenz für die Entdeckung Neuer und Neuartiger Materialien. Clubabend, Rotary Club Berlin-Unter den Linden, Berlin, Germany (2024)
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