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  1. M. Schilling-Wilhelmi, M. Ríos-García, S. Shabih, M. V. Gil, S. Miret, C. T. Koch, J. A. Márquez, and K. M. Jablonka
    From text to insight: large language models for chemical data extraction
    Chem. Soc. Rev. 54, 1125 (2025). [DOI] [arXiv]
  2. T. Denell, L. Himanen, M. Scheidgen, and C. Draxl
    Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering
    npj Comput Mater 11, 25 (2025). [DOI]
  3. 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]
  4. 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]
  5. 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
    J. Chem. Phys. 161, 024112 (2024). [DOI]
  6. 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, Ed. C. Hidalgo

    IOP Publishing , (2024). [DOI]
  7. M. Kuban, S. Rigamonti, and C. Draxl
    MADAS: A Python framework for assessing similarity in materials-science data
    Digit. Discov. 3, 2448 (2024). [DOI] [arXiv]
  8. M. L. Evans, J. Bergsma, A. Merkys, C. W. Andersen, O. B. Andersson, D. Beltrán, E. Blokhin, T. M. Boland, R. Castañeda Balderas, K. Choudhary, A. Díaz, R. Domínguez García, H. Eckert, K. Eimre, M. E. Fuentes Montero, A. M. Krajewski, J. Jørgen Mortensen, J. M. Nápoles Duarte, J. Pietryga, J. Qi, F. de Jesús Trejo Carrillo, A. Vaitkus, J. Yu, A. Zettel, P. B. de Castro, J. Carlsson, T. F. T. Cerqueira, S. Divilov, H. Hajiyani, F. Hanke, K. Jose, C. Oses, J. Riebesell, J. Schmidt, D. Winston, C. Xie, X. Yang, S. Bonella, S. Botti, S. Curtarolo, C. Draxl, L. E. Fuentes Cobas, A. Hospital, Z. Liu, M. A. L. Marques, N. Marzari, A. J. Morris, S. Ping Ong, M. Orozco, K. A. Persson, K. S. Thygesen, C. Wolverton, M. Scheidgen, C. Toher, G. J. Conduit, G. Pizzi, S. Gražulis, G. Rignanese, and R. Armiento
    Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
    Digit. Discov. 3, 1509 (2024). [DOI]
  9. A. Moshantaf, M. Wesemann, S. Beinlich, H. Junkes, J. Schumann, B. Alkan, P. Kube, C. P. Marshall, N. Pfister, and A. Trunschke
    Advancing catalysis research through FAIR data principles implemented in a local data infrastructure – a case study of an automated test reactor
    Catal. Sci. Technol. 14, 6186 (2024). [DOI]
  10. L. M. Ghiringhelli, L. Sbailò, Á. Fekete, M. Scheidgen, and M. Scheffler
    Choosing AI analysis tools and enacting their reproducibility: the NOMAD AI toolkit

    Section 3.4 in S. Bauer et al.

    Roadmap on Data-Centric Materials Science

    Modelling Simul. Mater. Sci. Eng. 32, 063301 (2024). [DOI]
  11. Y. Zimmermann et al. 
    Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry 
    preprint , (2024).  [arXiv]
  12. T. Bereau, L. J. Walter, and J. F. Rudzinski
    Martignac: Computational Workflows for Reproducible, Traceable, and Composable Coarse-Grained Martini Simulations
    J. Chem. Inf. Model. 64, 9413 (2024). [DOI] [arXiv]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. M. Krieger, H. B. Weber, and C. van Eldik
    Früh zur Datenkompetenz
    Phys. J. 21, 42 (2022).
  28. A. Trunschke
    Prospects and Challenges for Autonomous Catalyst Discovery Viewed from an Experimental Perspective
    Catal. Sci. Technol. 12, 3650 (2022). [DOI]
  29. 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]
  30. 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]
  31. L. M. Ghiringhelli
    An AI-toolkit to develop and share research into new materials
    Nat. Rev. Phys. 3, 724 (2021). [DOI]
  32. C. Draxl and M. Scheffler
    The NOMAD Laboratory: From Data Sharing to Artificial Intelligence
    J. Phys. Mater. 2, 036001 (2019). [DOI]
  33. 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]
  34. C. Draxl and M. Scheffler
    NOMAD: The FAIR Concept for Big-Data-Driven Materials Science
    MRS Bulletin 43, 676 (2018). [DOI] [arXiv]