FAIRMAT
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Selected talks and publications

 

 
  1. 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]
  2. 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]
  3. Markus Scheidgen, Lauri Himanen, Alvin Noe Ladines, David Sikter, Mohammad Nakhaee, Ádám Fekete, Theodore Chang, Amir Golparvar, José A. Márquez, Sandor Brockhauser, Sebastian Brückner, Luca M. Ghiringhelli, Felix Dietrich, Daniel Lehmberg, Thea Denell, Andrea Albino, Hampus Näsström, Sherjeel Shabih, Florian Dobener, Markus Kühbach, Rubel Mozumder, Joseph F. Rudzinski, Nathan Daelman, José M. Pizarro, Martin Kuban, Cuauhtemoc Salazar, Pavel Ondračka, Hans-Joachim Bungartz and Claudia Draxl 
    NOMAD: A distributed web-based platform for managing materials science research data
    J. Open Source Softw. 8, 5388 (2023). [DOI]
  4. Clara Patricia Marshall, Julia Schumann, Anette Trunschke
    Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use
    Angew. Chem. Int. Ed 62, e202302971 (2023). [DOI]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. M. Jalali, M.  Tsotsalas, and C. Wöll
    MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis
    Nanomaterials 12, 704 (2022). [DOI]