- 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
Scientific Data 10, 626 (2023). [DOI] - 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] - 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] - 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] - 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] - 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] - 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.
Electronic Structure 4, 023004 (2022). [DOI]
Roadmap on Machine Learning in Electronic Structure - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - L. M. Ghiringhelli
An AI-toolkit to develop and share research into new materials
Nat. Rev. Phys. 3, 724 (2021). [DOI] - C. Draxl and M. Scheffler
The NOMAD Laboratory: From Data Sharing to Artificial Intelligence
J. Phys. Mater. 2, 036001 (2019). [DOI] - 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] - C. Draxl and M. Scheffler
NOMAD: The FAIR Concept for Big-Data-Driven Materials Science
MRS Bulletin 43, 676 (2018). [DOI] [arXiv]