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  1. J. Schumann, H. Näsström, M. Götte, L. Himanen, A. Moshantaf, M. Scheidgen, J. A. Márquez Prieto, C. Draxl, and A. Trunschke
    Towards a new era for open and FAIR data in catalysis research – A catalysis plugin for NOMAD
    Chem Rxiv , (2025). [DOI]
  2. A. Maqsood, H. Näsström, C. Chen, L. Qiutong, J. Luo, R. Chakraborty, V. Blum, E. Unger, C. Draxl, J. A. Márquez, and T. Jesper Jacobsson
    Towards an interoperable perovskite description or how to keep track of 300 perovskite ions
    Nat Commun 16, 8725 (2025). [DOI]
  3. M. Jalali, A. D. Wonanke, P. Friederich, and C. Wöll
    The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal-Organic Framework Networks
    J. Chem. Inf. Model. , (2025). [DOI]
  4. N. Alampara, M. Schilling-Wilhelmi, M. Ríos-Garciía, I. Mandal, P. Khetarpal, H. S. Grover, N. M. Anoop Krishnan, and K. M. Jablonka
    Probing the limitations of multimodal language models for chemistry and materials research
    Nat Comput Sci 36, (2025). [DOI]
  5. A. Mirza, N. Alampara, S. Kunchapu, M. Ríos-García, B. Emoekabu, A. Krishnan, T. Gupta, M. Schilling-Wilhelmi, M. Okereke, A. Aneesh, M. Asgari, J. Eberhardt, A. M. Elahi, H. M. Elbeheiry, M. V. Gil, C. Glaubitz, M. Greiner, C. T. Holick, T. Hoffmann, A. Ibrahim, L. C. Klepsch, Y. Köster, A. Kreth, J. Meyer, S. Miret, J. M. Peschel, M. Ringleb, N. C. Roesner, J. Schreiber, U. S. Schubert, L. M. Stafast, D. Wonanke, M. Pieler, P. Schwaller, K. M. Jablonka
    A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists
    Nat. Chem 17, 1027 (2025). [DOI]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. Y. Zimmermann et al. 
    Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry 
    preprint , (2024).  [arXiv]
  15. 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]
  16. 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]
  17. Mehrdad Jalali, A.D. Dinga Wonanke, Christof Wöll
    MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks
    J. Cheminform. 15, 94 (2023). [DOI]
  18. M. Scheidgen, L. Himanen, A. N. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. A. Márquez, S. Brockhauser, S. Brückner, L. M. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. F. Rudzinski, N. Daelman, J. M. Pizarro, M. Kuban, C. Salazar, P. Ondračka, H.-J. Bungartz, and C. Draxl 
    NOMAD: A distributed web-based platform for managing materials science research data
    J. Open Source Softw. 8, 5388 (2023). [DOI]
  19. C. P. Marshall, J. Schumann, and A. Trunschke
    Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use
    Angew. Chem. Int. Ed 62, e202302971 (2023). [DOI]
  20. 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]
  21. 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]
  22. 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]
  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. Y. Luo, S. Bag, O. Zaremba, A. Cierpka, J. Andreo, S. Wuttke, P. Friederich, and M. Tsotsalas
    MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning
    Angew. Chem. Int. Ed. 61, e202200242 (2022). [DOI]
  26. M. Jalali, M.  Tsotsalas, and C. Wöll
    MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis
    Nanomaterials 12, 704 (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]