Researcher Portfolio

 
   

Prof. Dr. Scheffler, Matthias

NOMAD, Fritz Haber Institute, Max Planck Society, Physikalisch-Technische Bundesanstalt, Theory, Fritz Haber Institute, Max Planck Society  

 

Researcher Profile

 
Position: Theory, Fritz Haber Institute, Max Planck Society
Position: NOMAD, Fritz Haber Institute, Max Planck Society
Position: Physikalisch-Technische Bundesanstalt
Additional IDs: ORCID: https://orcid.org/0000-0002-1280-9873
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons22064

External references

 
WorldCat Search for Scheffler, Matthias
Google Scholar Search for Scheffler, Matthias

Publications

 
  (1 - 25 of 895)
 : Moerman, E., Gallo, A., Irmler, A., Schäfer, T., Hummel, F., Grüneis, A., & Scheffler, M. (2025). 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. doi:10.1021/acs.jctc.4c01451. [PubMan] : Bellini, G., Koch, G., Girgsdies, F., Dong, J., Carey, S., Timpe, O., Auffermann, G., Scheffler, M., Schlögl, R., Foppa, L., & Trunschke, A. (2025). CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and Artificial Intelligence. Angewandte Chemie International Edition, 64(6): e202417812. doi:10.26434/chemrxiv-2024-8xkh5. [PubMan] : Mauß, J. M., Kley, K. S., Khobragade, R., Tran, N. K., De Bellis, J., Schüth, F., Scheffler, M., & Foppa, L. (in preparation). Modelling the Time-Dependent Reactivity of Catalysts by Experiments and Artificial Intelligence. [PubMan] : Speckhard, D., Carbogno, C., Ghiringhelli, L. M., Lubeck, S., Scheffler, M., & Draxl, C. (2025). Extrapolation to the complete basis-set limit in density-functional theory using statistical learning. Physical Review Materials, 9(1): 013801. doi:10.1103/PhysRevMaterials.9.013801. [PubMan] : Sugathan Nair, A., Foppa, L., & Scheffler, M. (in preparation). Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis. [PubMan] : Miyazaki, R., Faraji, S., Levchenko, S. V., Foppa, L., & Scheffler, M. (2024). 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. doi:10.26434/chemrxiv-2024-k9mq6-v3. [PubMan] : Quan, J., Carbogno, C., & Scheffler, M. (2024). Carrier Mobility of Strongly Anharmonic Materials from First Principles. Physical Review B, 110(23): 235202. doi:10.1103/PhysRevB.110.235202. [PubMan] : Behler, J., Csanyi, G., Foppa, L., Kang, K., Langer, M. F., Margraf, J. T., Sugathan Nair, A., Purcell, T. A. R., Rinke, P., Scheffler, M., Tkatchenko, A., Todorovic, M., Unke, O. T., & Yao, Y. (in preparation). Workflows for Artificial Intelligence. [PubMan] : Quan, J., Zhang, M.-Y., Scheffler, M., & Carbogno, C. (in preparation). Temperature-Dependent Electronic Spectral Functions From Band-Structure Unfolding. [PubMan] : Moerman, E., & Scheffler, M. (in preparation). Coupled-Cluster Theory for the Ground State and for Excitations. [PubMan] : Kang, K., Scheffler, M., Carbogno, C., & Purcell, T. (in preparation). Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials Through Active Learning. [PubMan] : Bauer, S., Benner, P., Bereau, T., Blum, V., Boley, M., Carbogno, C., Catlow, C. R. A., Dehm, G., Eibl, S., Ernstorfer, R., Fekete, A., Foppa, L., Fratzl, P., Freysoldt, C., Gault, B., Ghiringhelli, L. M., Giri, S. K., Gladyshev, A., Goyal, P., Hattrick-Simpers, J., Kabalan, L., Karpov, P., Khorrami, M. S., Koch, C., Kokott, S., Kosch, T., Kowalec, I., Kremer, K., Leitherer, A., Li, Y., Liebscher, C. H., Logsdail, A. J., Lu, Z., Luong, F., Marek, A., Merz, F., Mianroodi, J. R., Neugebauer, J., Pei, Z., Purcell, T., Raabe, D., Rampp, M., Rossi, M., Rost, J.-M., Saal, J. E., Saalmann, U., Sasidhar, K. N., Saxena, A., Sbailo, L., Scheidgen, M., Schloz, M., Schmidt, D. F., Teshuva, S., Trunschke, A., Wei, Y., Weikum, G., Xian, R. P., Yao, Y., Yin, J., Zhao, M., & Scheffler, M. (2024). Roadmap on data-centric materials science. Modelling and Simulation in Materials Science and Engineering, 32(6): 063301. doi:10.1088/1361-651X/ad4d0d. [PubMan] : Kokott, S., Merz, F., Yao, Y., Carbogno, C., Rossi, M., Havu, V., Rampp, M., Scheffler, M., & Blum, V. (2024). Efficient all-electron hybrid density functionals for atomistic simulations beyond 10 000 atoms. The Journal of Chemical Physics, 161(2): 024112. doi:10.1063/5.0208103. [PubMan] : Foppa, L., & Scheffler, M. (in preparation). Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups. [PubMan] : Miyazaki, R., Belthle, K. S., Tüysüz, H., Foppa, L., & Scheffler, M. (2024). 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. doi:10.1021/jacs.3c12984. [PubMan] : Bi, S., Carbogno, C., Zhang, I. Y., & Scheffler, M. (2024). Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework. The Journal of Chemical Physics, 160(3): 034106. doi:10.1063/5.0178075. [PubMan] : Boley, M., Luong, F., Teshuva, S., Schmidt, D. F., Foppa, L., & Scheffler, M. (in preparation). From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery. [PubMan] : Foppa, L., & Scheffler, M. (in preparation). Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance. [PubMan] : Dean, J., Scheffler, M., Purcell, T., Barabash, S. V., Bhowmik, R., & Bazhirov, T. (2023). Interpretable Machine Learning for Materials Design. Journal of Materials Research, 38(20), 4477-4496. doi:10.1557/s43578-023-01164-w. [PubMan] : Purcell, T., Scheffler, M., & Ghiringhelli, L. M. (2023). Recent advances in the SISSO method and their implementation in the SISSO++ Code. The Journal of Chemical Physics, 159(11): 114110. doi:10.1063/5.0156620. [PubMan] : Gavini, V., Baroni, S., Blum, V., Bowler, D. R., Buccheri, A., Chelikowsky, J. R., Das, S., Dawson, W., Delugas, P., Dogan, M., Draxl, C., Galli, G., Genovese, L., Giannozzi, P., Giantomassi, M., Gonze, X., Govoni, M., Gygi, F., Gulans, A., Herbert, J. M., Kokott, S., Kühne, T. D., Liou, K.-H., Miyazaki, T., Motamarri, P., Nakata, A., Pask, J. E., Plessl, C., Ratcliff, L. E., Richard, R. M., Rossi, M., Schade, R., Scheffler, M., Schütt, O., Suryanarayana, P., Torrent, M., Truflandier, L., Windus, T. L., Xu, Q., Yu, V.-W.-.-Z., & Perez, D. (2023). Roadmap on electronic structure codes in the exascale era. Modelling and Simulation in Materials Science and Engineering, 31(6): 063301. doi:10.1088/1361-651X/acdf06. [PubMan] : Langer, M. F., Knoop, F., Carbogno, C., Scheffler, M., & Rupp, M. (2023). Heat flux for semilocal machine-learning potentials. Physical Review B, 108(10): L100302. doi:10.1103/PhysRevB.108.L100302. [PubMan] : Ghiringhelli, L. M., Baldauf, C., Bereau, T., Brockhauser, S., Carbogno, C., Chamanara, J., Cozzini, S., Curtarolo, S., Draxl, C., Dwaraknath, S., Fekete, Á., Kermode, J., Koch, C. T., Kühbach, M., Ladines, A. N., Lambrix, P., Himmer, M.-O.-L., Levchenko, S. V., Oliveira, M., Michalchuk, A., Miller, R., Onat, B., Pavone, P., Pizzi, G., Regler, B., Rignanese, G.-M., Schaarschmidt, J., Scheidgen, M., Schneidewind, A., Sheveleva, T., Su, C., Usvyat, D., Valsson, O., Wöll, C., & Scheffler, M. (2023). Shared Metadata for Data-Centric Materials Science. Scientific Data, 10: 626. doi:10.1038/s41597-023-02501-8. [PubMan] : Lu, S., Ghiringhelli, L. M., Carbogno, C., Wang, J., & Scheffler, M. (in preparation). On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials. [PubMan] : Purcell, T., Scheffler, M., Ghiringhelli, L. M., & Carbogno, C. (2023). Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence. npj Computational Materials, 9: 112. doi:10.1038/s41524-023-01063-y. [PubMan]