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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. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. M, Azizi, J. Wilhelm, D. Golze, M. Giantomassi, R. L. Panadés-Barrueta, F. A. Delesma, A. Buccheri, A. Gulans, P. Rinke, C. Draxl, and X. Gonze 
    Time-frequency component of the GreenX library: minimax grids for efficient RPA and GW calculations
    Journal of Open Source Software 8, 5570 (2023). [DOI]
  14. W. C. Witt, C. van der Oord, E. Gelžinytė, T. Järvinen, A. Ross, J. P. Darby, C. H. Ho, W. J. Baldwin, M. Sachs, J. Kermode, N. Bernstein, G. Csányi, C. Ortner
    ACEpotentials.jl: A Julia implementation of the atomic cluster expansion
    J. Chem. Phys. 159, 164101 (2023). [DOI]
  15. S. Klawohn, G. Csányi, J. P. Darby, J. R. Kermode, M. A. Caro, A. P. Bartók
    Gaussian Approximation Potentials: theory, software implementation and application examples
    J. Chem. Phys. , Accepted (2023).  [arXiv]
  16. A. Marek, M. Rampp, K. Reuter, and E. Laure.
    Beyond the Fourth Paradigm — the Rise of AI
    2023 IEEE 19th International Conference on e-Science (e-Science), Limassol, Cyprus , 1-4 (2023). [DOI]
  17. A. P. Bartók and J. R. Kermode
    Improved Uncertainty Quantification for Gaussian Process Regression Based Interatomic Potentials
    Preprint , (2022).  [arXiv]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. J. P. Darby, J. R. Kermode, and G. Csányi
    Compressing Local Atomic Neighbourhood Descriptors
    npj Comput. Mater. 8, 166 (2022). [DOI] [arXiv]
  24. 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]
  25. 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]
  26. 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]
  27. 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]
  28. A. Gulans and C. Draxl
    Influence of spin-orbit coupling on chemical bonding
    Preprint , (2022).  [arXiv]
  29. 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]
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. 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]
  37. 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]
  38. 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]
  39. 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]
  40. 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]
  41. 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]
  42. 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]
  43. 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]
  44. 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]
  45. 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]
  46. 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]
  47. 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]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. L. M. Ghiringhelli
    An AI-toolkit to develop and share research into new materials
    Nat. Rev. Phys. 3, 724 (2021). [DOI]
  53. 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]
  54. 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]
  55. 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]
  56. 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]
  57. 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]
  58. 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]
  59. 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]
  60. 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]