Scientific 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

   Preprint , (2022).  [arXiv]

2. 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]

3. 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]

4. 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]

5. 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. , (2022). [DOI] [arXiv]

6. 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, 1 (2022). [DOI] [arXiv]

7. 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, (2022). [DOI]

8. M. Jalali, M.  Tsotsalas, and C. Wöll

   MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

   Nanomaterials 12, 704 (2022). [DOI]

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