1. 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 , in print (2023). [DOI]
  2. A. P. Bartók and J. R. Kermode

    Improved Uncertainty Quantification for Gaussian Process Regression Based Interatomic Potentials

    Preprint , (2022).  [arXiv]
  3. S. Klawohn, J. R. Kermode, and A. P. Bartók

    Massively Parallel Fitting of Gaussian Approximation Potentials

    Preprint , (2022).  [arXiv]
  4. 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]
  5. J. Li, Y. Jin, P. Rinke, W. Yang, and D. Golze

    Benchmark of GW Methods for Core-Level Binding Energies

    Preprint , (2022).  [arXiv]
  6. 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

    Preprint , (2022).  [arXiv]
  7. 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]
  8. 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]
  9. 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]
  10. J. P. Darby, J. R. Kermode, and G. Csányi

    Compressing Local Atomic Neighbourhood Descriptors

    npj Comput. Mater. 8, 166 (2022). [DOI] [arXiv]
  11. 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]
  12. 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

    Preprint , (2022).  [arXiv]
  13. 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]
  14. 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]
  15. A. Gulans and C. Draxl

    Influence of spin-orbit coupling on chemical bonding

    Preprint , (2022).  [arXiv]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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

    Preprint , (2022).  [arXiv]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. F. Knoop, M. Scheffler, and C. Carbogno

    Ab initio Green-Kubo simulations of heat transport in solids: method and implementation

    Preprint , (2022).  [arXiv]
  26. F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno

    Anharmonicity in Thermal Insulators – An Analysis from First Principles

    Preprint , (2022).  [arXiv]
  27. 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]
  28. 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]
  29. 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]
  30.  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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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 , (2021). [DOI]
  36. 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]
  37. 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]
  38. 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]
  39. 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]
  40. 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]
  41. 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]
  42. L. M. Ghiringhelli

    An AI-toolkit to develop and share research into new materials

    Nat. Rev. Phys. 3, 724 (2021). [DOI]
  43. 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]
  44. 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]
  45. 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]
  46. 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]
  47. 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]
  48. 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]
  49. 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]
  50. 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]