FAIRMAT
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Selected talks and publications

 

 
  1. S. Bi, C. Carbogno, I. Y. Zhang, M. Scheffler
    Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework
    J. Chem. Phys. 160, 034106 (2024). [DOI]
  2. A. D. Fuchs, J. A. F. Lehmeyer, H. Junkes, H. B. Weber, and M. Krieger
    NOMAD CAMELS: Configurable Application for Measurements, Experiments and Laboratory Systems
    J. Open Source Softw. 9, 6371 (2024). [DOI]
  3. S. Kokott, F. Merz, Y. Yao, C. Carbogno, M. Rossi, M, Rampp, V. Havu, M. Scheffler, V. Blum
    Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms
    Preprint , (2024).  [arXiv]
  4. M. Baldovin, A. Browaeys, J.M. De Teresa, C. Draxl, F. Druon, F. Fradenigo, J.-J. Freffet, F. Lépine, J. Lüning, L. Reining, P. Salières, P. Seneor, L. Silva, T. Tschentscher, K. van Der Beek, A. Vollmer, and A. Vulpiani
    Matter and Waves, Chapter 3 in EPS Grand Challenges -  Physics for Society in the Horizon 2050
    IOP Publishing 1, 120 (2024). [DOI]
  5. M. Kuban, S. Rigamonti, C. Draxl
    MADAS: A Python framework for assessing similarity in materials-science data
    , 12 (2024). [DOI]
  6. L.M. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C.T. Koch, M. Kühbach, A.N. Ladines, P. Lambrix, M.O. Lenz-Himmer, S. Levchenko, M. Oliveira, A. Michalchuk, R. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G.M. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, and M. Scheffler
    Shared Metadata for Data-Centric Materials Science
    Sci. Data 10, 626 (2023). [DOI]
  7. V. Gavini, 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, A. Gulans, F. Gygi, 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 Simul. Mater. Sci. Eng. 31, 063301 (2023). [DOI]
  8. T.A.R. Purcell, M. Scheffler, L.M. Ghiringhelli
    Recent advances in the SISSO method and their implementation in the SISSO++ code
    J. Chem. Phys. 159, 114110 (2023). [DOI]
  9. F. Knoop, T.A.R. Purcell, M. Scheffler, C. Carbogno
    Anharmonicity in Thermal Insulators: An Analysis from First Principles
    Phys. Rev. Lett. 130, 236301 (2023). [DOI]
  10. H. Lu, G. Koknat, Y. Yao, J. Hao, X. Qin, C. Xiao, R. Song, F. Merz, M. Rampp, S. Kokott, C. Carbogno, T. Li, G. Teeter, M. Scheffler, J. J. Berry, D. B. Mitzi, J. L. Blackburn, V. Blum, and M. C. Beard
    Electronic Impurity Doping of a 2D Hybrid Lead Iodide Perovskite by Bi and Sn
    PRX Energy 2, 023010 (2023). [DOI]
  11. F. Knoop, M. Scheffler, C. Carbogno
    Ab initio Green-Kubo simulations of heat transport in solids: Method and implementation
    Phys. Rev. B 107, 224304 (2023). [DOI]
  12. M. Boley, F. Luong, S. Teshuva, D. F. Schmidt, L. Foppa, M. Scheffler
    From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery
    Submitted , (2023).  [arXiv]
  13. C. Draxl, M. Kuban, S. Rigamonti, and M. Scheidgen
    Challenges and perspectives for interoperability and reuse of heterogenous data collections

    Section 4.1 in H. J. Kulik, et al.
    Roadmap on Machine Learning in Electronic Structure

    Electronic Structure 4, 023004 (2022). [DOI]
  14. M. Kuban, S. Rigamonti, M. Scheidgen, and C. Draxl
    Density-of-states similarity descriptor for unsupervised learning from materials data
    Sci. Data 9, 646 (2022). [DOI] [arXiv]
  15. A. Mazheika, Y. Wang, R. Valero, F. Vines, F. Illas, L. Ghiringhelli, S. Levchenko, and M. Scheffler
    Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides
    Nat. Commun. 13, 416 (2022). [DOI]
  16. M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H.-J. Bungartz, C.Felser, M. Greiner, A. Groß, C. Koch, K. Kremer, W. E. Nagel, M. Scheidgen, C. Wöll, and C. Draxl
    FAIR data enabling new horizons for materials research
    Nature 604, 635 (2022). [DOI] [arXiv]
  17. A. M. Teale, T. Helgaker, A. Savin, C. Adamo,  B. Aradi, A. V. Arbuznikov, P. W. Ayers, E. J. Baerends, V. Barone, P. Calaminici, E. Cancès, E. A. Carter, P. K. Chattaraj, H. Chermette, I. Ciofini, T. D. Crawford, F. De Proft, J. F. Dobson, C. Draxl, T. Frauenheim, E. Fromager, P. Fuentealba, L. Gagliardi, G. Galli, J. Gao, P. Geerlings,  N. Gidopoulos, P. M. W. Gill, P. Gori-Giorgi, A. Görling,  T. Gould,  S. Grimme, O. Gritsenko, H. J. A.Jensen, E. R. Johnson, R. O. Jones, M. Kaupp,  A. M. Köster,  L. Kronik,  A. I. Krylov, S. Kvaal,  A. Laestadius, M. Levy, M. Lewin,  S. Liu, P.-F. Loos, N. T. Maitra, F. Neese, J. P. Perdew,  K. Pernal, P. Pernot, P. Piecuch, E. Rebolini, L. Reining,  P. Romaniello, A. Ruzsinszky,  D. R. Salahub, M. Scheffler,  P. Schwerdtfeger, V. N. Staroverov, J. Sun, E. Tellgren, D. J. Tozer, S. B. Trickey, C. A. Ullrich,  A. Vela, G. Vignale, T. A. Wesolowski, and X. W. Yang
    DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science
    Phys. Chem. Chem. Phys. 47, 28700 (2022). [DOI] [arXiv]
  18. M. Kuban, Š. Gabaj, W. Aggoune, C. Vona, S. Rigamonti, and C. Draxl
    Similarity of materials and data‑quality assessment by fingerprinting

    MRS Bulletin Impact section

    MRS Bulletin 47, 991 (2022). [DOI] [arXiv]
  19. C. Carbogno, K.S. Thygesen, B. Bieniek, C. Draxl, L.M. Ghiringhelli, A. Gulans, O. T. Hofmann, K. W. Jacobsen, S. Lubeck, J. J. Mortensen, M. Strange, E. Wruss, and M. Scheffler
    Numerical Quality Control for DFT-based Materials Databases
    npj Computational Materials 8, 69 (2022). [DOI]
  20. C. W. Andersen, R. Armiento, E. Blokhin, G. J. Conduit, S. Dwaraknath, M. L. Evans, Á. Fekete, A. Gopakumar, S. Gražulis, A. Merkys, F. Mohamed, C. Oses, G. Pizzi, G.-M. Rignanese, M. Scheidgen, L. Talirz, C. Toher, D. Winston, R. Aversa, K. Choudhary, P. Colinet, S. Curtarolo, D. Di Stefano, C. Draxl, S. Er, M. Esters, M. Fornari, M. Giantomassi, M. Govoni, G. Hautier, V. Hegde, M. K. Horton, P. Huck, G. Huhs, J. Hummelshøj, A. Kariryaa, B. Kozinsky, S. Kumbhar, M. Liu, N. Marzari, A. J. Morris, A. Mostofi, K. A. Persson, G. Petretto, T. Purcell, F. Ricci, F. Rose, M. Scheffler, D. Speckhard, M. Uhrin, A. Vaitkus, P. Villars, D. Waroquiers, C. Wolverton, M. Wu, and X. Yang
    OPTIMADE: an API for exchanging materials data
    Scientific Data 8, 217 (2021). [DOI] [arXiv]
  21. M. L. Evans, C. W. Andersen, S. Dwaraknath, M. Scheidgen, Á. Fekete, and D. Winston
    optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs
    J. of Open Source Softw. 6, 3458 (2021). [DOI]
  22. L. M. Ghiringhelli
    An AI-toolkit to develop and share research into new materials
    Nat. Rev. Phys. 3, 724 (2021). [DOI]
  23. C. Draxl and M. Scheffler
    The NOMAD Laboratory: From Data Sharing to Artificial Intelligence
    J. Phys. Mater. 2, 036001 (2019). [DOI]
  24. C. Draxl and M. Scheffler
    Big-Data-Driven Materials Science and its FAIR Data Infrastructure
    Handbook of Materials Modeling (Andreoni W., Yip S. eds), Springer, Cham , (2019). [DOI] [arXiv]
  25. C. Draxl and M. Scheffler
    NOMAD: The FAIR Concept for Big-Data-Driven Materials Science
    MRS Bulletin 43, 676 (2018). [DOI] [arXiv]