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  1. S. Klawohn, J. R. Kermode, and A. P. Bartók
    Massively Parallel Fitting of Gaussian Approximation Potentials Mach. Learn. Sci. Tech. 4, 015020 (2023). [DOI]
  2. T. Purcell, M. Scheffler, L. M. Ghiringhelli, and C. Carbogno
    Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity npj Computational Materials 9, 112 (2023). [DOI]
  3. T. Barnard, S. Tseng, J.P. Darby, A.P. Bartók, A. Broo, and G.C. Sosso
    Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor Mol. Syst. Des. Eng., Advance Article 8, 300-315 (2023). [DOI]
  4. J. P. Darby, D. P. Kovács, I. Batatia, M. A. Caro, G. L. W. Hart, C. Ortner, and G. Csányi
    Tensor-Reduced Atomic Density Representations Phys. Rev. Lett. 131, 028001 (2023). [DOI]
  5. A. Buccheri, F. Peschel, B. Maurer, M. Voiculescu, D. T. Speckhard, H. Kleine, E. Stephan, M. Kuban, and C. Draxl,
    excitingtools: An exciting Workflow Tool JOSS 8, 5148 (2023). [DOI]
  6. T.A.R. Purcell, M. Scheffler, L.M. Ghiringhelli
    Recent advances in the SISSO method and their implementation in the SISSO++ code Preprint , (2023).  [arXiv]
  7. 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]
  8. 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]
  9. 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]
  10. J. Laakso, L. Himanen, H. Homm, E. V. Morooka, M. O. J. Jäger, M. Todorović, P. Rinke
    Updates to the DScribe library: New descriptors and derivatives J. Chem. Phys. 158, 234802 (2023). [DOI]
  11. A. P. Bartók and J. R. Kermode
    Improved Uncertainty Quantification for Gaussian Process Regression Based Interatomic Potentials Preprint , (2022).  [arXiv]
  12. L. Zhang, B. Onat, G. Dusson, G. Anand, R. J. Maurer, C. Ortner, and J.R. Kermode
    Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models npj Comput. Mater. 8, 158 (2022). [DOI] [arXiv]
  13. J. Li, Y. Jin, P. Rinke, W. Yang, and D. Golze
    Benchmark of GW Methods for Core-Level Binding Energies J. Chem. Theory Comput. 18, 7570–7585 (2022). [DOI]
  14. H. Moustafa, P.M. Larsen, M.N. Gjerding, J.J. Mortensen, K.S. Thygesen, and K.W. Jacobsen
    Computational exfoliation of atomically thin 1D materials with application to Majorana bound states Phys. Rev. Materials 6, 064202 (2022). [DOI]
  15. 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]
  16. F. Bertoldo, S. Ali, S. Manti, and K.S. Thygesen
    Quantum point defects in 2D materials: The QPOD database npj Comput. Mater. 8, 56 (2022). [DOI] [arXiv]
  17. M. Boley and M. Scheffler
    Learning Rules for Materials Properties and Functions

    Section 1.4 in H. J. Kulik, et al. Roadmap on Machine Learning in Electronic Structure

    Electronic Structure 4, 023004 (2022). [DOI]
  18. J. P. Darby, J. R. Kermode, and G. Csányi
    Compressing Local Atomic Neighbourhood Descriptors npj Comput. Mater. 8, 166 (2022). [DOI] [arXiv]
  19. 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]
  20. L. Foppa, T. A. R. Purcell, S. V. Levchenko, M. Scheffler, and L. M. Ghiringhelli
    Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites Phys. Rev. Lett. 129, 055301 (2022). [DOI]
  21. L. Foppa, C. Sutton, L. M. Ghiringhelli, S. De, P. Löser, S.A. Schunk, A. Schäfer, and M. Scheffler
    Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence ACS Catalysis 12, 2223 (2022). [DOI]
  22. L.M. Ghiringhelli
    Interpretability of machine-learning models in physical sciences

    Section 5.3 in H. J. Kulik, et al.
    Roadmap on Machine Learning in Electronic Structure

    Electronic Structure 4, 023004 (2022). [DOI] [arXiv]
  23. A. Gulans and C. Draxl
    Influence of spin-orbit coupling on chemical bonding Preprint , (2022).  [arXiv]
  24. N. R. Knosgaard and K. S. Thygesen
    Representing individual electronic states for machine learning GW band structures of 2D materials Nat. Commun. 13, 468 (2022). [DOI] [arXiv]
  25. 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]
  26. 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]
  27. E. Moerman, F. Hummel, A. Grüneis, A. Irmler, and M. Scheffler
    Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions J. Open Source Softw. 7, 4040 (2022). [DOI] [arXiv]
  28. 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]
  29. 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]
  30. Y. Zhou, C. Zhu, M. Scheffler, and L. M. Ghiringhelli
    Ab initio approach for thermodynamic surface phases with full consideration of anharmonic effects – the example of hydrogen at Si(100) Phys. Rev. Lett. 128, 246101 (2022). [DOI] [arXiv]
  31. 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]
  32. B. Hoock, S. Rigamonti, and C. Draxl
    Advancing descriptor search in materials science: feature engineering and selection strategies New J. Phys. 24, 113049 (2022). [DOI] [arXiv] [data]
  33. D. Zavickis, K. Kacars, J. Cīmurs, and A. Gulans
    Adaptively compressed exchange in the linearized augmented plane wave formalism Phys. Rev. B 106, 165101 (2022). [DOI] [arXiv]
  34. 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]
  35.  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 Model. Simul. Mat. Sci. Eng. , (2022).  [arXiv]
  36. M. Bowker, S. DeBeer, N.F. Dummer, G.J. Hutchings, M. Scheffler, F. Schüth, S.H. Taylor, and H. Tüysüz
    Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT) Angew. Chem. Int. Ed. 61, e202209016 (2022). [DOI]
  37. J. Kangsabanik, M.K. Svendsen, A. Taghizadeh, A. Crovetto, and K.S. Thygesen
    Indirect Band Gap Semiconductors for Thin-Film Photovoltaics: High-Throughput Calculation of Phonon-Assisted Absorption J. Am. Chem. Soc. 144, 19872 (2022). [DOI]
  38. L. Sbailò, Á. Fekete, L.M. Ghiringhelli, and M. Scheffler
    The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding npj Comput. Mater. 8, 250 (2022). [DOI]
  39. X. Liu, P.-P. De Breuck, L. Wang and G.-M. Rignanese
    A simple denoising approach to exploit multi-fidelity data for machine learning materials properties npj Computational Materials 8, 233 (2022). [DOI]
  40. H. Shang, X. Duan, F. Li, L. Zhang, Z. Xu, K. Liu, H. Luo, Y. Ji, W. Zhao, W. Xue, L. Chen, and Y. Zhang
    Many-core acceleration of the first-principles all-electron quantum perturbation calculations Comp. Phys. Commun. 267, 108045 (2021). [DOI]
  41. M. Gjerding, T. Skovhus, A. Rasmussen, F. Bertoldo, A.H. Larsen, J.J. Mortensen, and K.S. Thygesen
    Atomic Simulation Recipes - a Python framework and library for automated workflows Psi-k Scientific Highlight Of The Month 199, 110731 (2021). [DOI]
  42. T. Schäfer, A. Gallo, A. Irmler, F. Hummel, and A. Grüneis
    Surface science using coupled cluster theory via local Wannier functions and in-RPA-embedding: The case of water on graphitic carbon nitride J. Chem. Phys. 155, 244103 (2021). [DOI] [arXiv]
  43. P.-P. De Breuck, M. L. Evans, and G.-M. Rignanese
    Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet J. Phys.: Condens. Matter 33, 404002 (2021). [DOI] [arXiv]
  44. 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]
  45. 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]
  46. L. Foppa, L.M. Ghiringhelli, F. Girgsdies, M. Hashagen, P. Kube, M. Hävecker, S. Carey, A. Tarasov, P. Kraus, F. Rosowski, R. Schlögl, A. Trunschke, and M. Scheffler
    Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence MRS Bulletin 46, 1016 (2021). [DOI]
  47. L. Foppa and L. M. Ghiringhelli
    Identifying outstanding transition-metal-alloy heterogeneous catalysts for the oxygen reduction and evolution reactions via subgroup discovery Top. Catal. 65, 196 (2021). [DOI]
  48. L. M. Ghiringhelli
    An AI-toolkit to develop and share research into new materials Nat. Rev. Phys. 3, 724 (2021). [DOI]
  49. M. Gjerding, T. Skovhus, A. Rasmussen, F. Bertoldo, A. H. Larsen, J. J. Mortensen, K. S. Thygesen
    Atomic Simulation Recipes: A Python framework and library for automated workflows Comput. Mater. Sci. 199, 110731 (2021). [DOI]
  50. M. N. Gjerding, A. Taghizadeh, A. Rasmussen, S. Ali, F. Bertoldo, T. Deilmann, N. R. Knøsgaard, M. Kruse, A. H. Larsen, S. Manti, T. G. Pedersen, U. Petralanda, T. Skovhus, M. K. Svendsen, J. J. Mortensen, T. Olsen, and K. S. Thygesen
    Recent progress of the Computational 2D Materials Database (C2DB) 2d Mater. 8, 044002 (2021). [DOI]
  51. S. Kokott, I. Hurtado, C. Vorwerk, C. Draxl, V. Blum, and M. Scheffler
    GIMS: Graphical Interface for Materials Simulations J. Open Source Softw. 6, 2767 (2021). [DOI]
  52. A. Leitherer, A. Ziletti, and L.M. Ghiringhelli
    Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning Nat. Commun. 12, 6234 (2021). [DOI]
  53. A. Rasmussen, T. Deilmann, and K. S. Thygesen,
    Towards fully automatized GW band structure calculations: What we can learn from 60.000 self-energy evaluations npj Comput. Mater. 7, 22 (2021). [DOI]
  54. X. Ren, F. Merz, H. Jiang, Y. Yao, M. Rampp, H. Lederer, V. Blum, and M. Scheffler
    All-electron periodic G(0)W(0) implementation with numerical atomic orbital basis functions: Algorithm and benchmarks Phys. Rev. Mater. 5, 013807 (2021). [DOI]
  55. L. Schmidt-Mende, V. Dyakonov, S. Olthof, F. Ünlü, K. Moritz, T. Lê, S. Mathur, A. D. Karabanov, D. C. Lupascu, L. Herz, A. Hinderhofer, F. Schreiber, A. Chernikov, D. A. Egger, O. Shargaieva, C. Cocchi, E. Unger, M. Saliba, M. Malekshahi Byranvand, M. Kroll, F. Nehm, K. Leo, A. Redinger, J. Höcker, T. Kirchartz, J. Warby, E. Gutierrez-Partida, D. Neher, M. Stolterfoht, U. Würfel, M. Unmüssig, J. Herterich, C. Baretzky, J. Mohanraj, M. Thelakkat, C. Maheu, W. Jaegermann, T. Mayer, J. Rieger, T. Fauster, D. Niesner, F. Yang, S. Albrecht, T. Riedl, A. Fakharuddin, M. Vasilopoulou, Y. Vaynzof, D. Moia, J. Maier, M.Franckevi ̆cius, V. Gulbinas, R. A. Kerner, L. Zhao, B. P. Rand, N. Glück, T. Bein, F. Matteocci, L. Angelo Castriotta, A. Di Carlo, M. Scheffler, and C. Draxl
    Roadmap: Organic-inorganic hybrid perovskite semiconductors and devices APL Materials 9, 109202 (2021). [DOI] [arXiv]
  56. B. Onat, C. Ortner, and J.R. Kermode
    Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials J. Chem. Phys. 153, 144106 (2020). [DOI] [arXiv]