1. A. P. Bartók and J. R. Kermode

    Improved Uncertainty Quantification for Gaussian Process Regression Based Interatomic Potentials

    Preprint , (2022). [DOI] [Download]
  2. S. Klawohn, J. R. Kermode, and A. P. Bartók

    Massively Parallel Fitting of Gaussian Approximation Potentials

    Preprint , (2022). [DOI] [Download]
  3. 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 Computational Materials 8, 158 (2022). [DOI] [Download]
  4. J. Li, Y. Jin, P. Rinke, W. Yang, D. Golze

    Benchmark of GW Methods for Core-Level Binding Energies

    Preprint , (2022). [DOI] [Download]
  5. H. Moustafa, P.M. Larsen, M.N. Gjerding, J.J. Mortensen, K.S. Thygesen, K.W. Jacobsen

    Computational exfoliation of atomically thin 1D materials with application to Majorana bound states

    Preprint , (2022). [DOI] [Download]
  6. 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, M. Scheffler

    Shared Metadata for Data-Centric Materials Science

    Preprint , (2022). [DOI] [Download]
  7. F. Bertoldo, S. Ali, S. Manti, K. S. Thygesen

    Quantum point defects in 2D materials: The QPOD database

    npj Computational Materials 8, 56 (2022). [DOI] [Download]
  8. 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).  [Download]
  9. J. P. Darby, J. R. Kermode and G. Csányi

    Compressing Local Atomic Neighbourhood Descriptors

    npj Computational Materials 8, 166 (2022). [DOI] [Download]
  10. 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).  [Download]
  11. 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). [DOI] [Download]
  12. 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] [Download]
  13. 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] [Download]
  14. A. Gulans and C. Draxl

    Influence of spin-orbit coupling on chemical bonding

    Preprint , (2022). [DOI] [Download]
  15. N. R. Knosgaard and K. S. Thygesen

    Representing individual electronic states for machine learning GW band structures of 2D materials

    Nature Communications 13, Article number: 468 (2022). [DOI] [Download]
  16. 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] [Download]
  17. 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

    Nature Communications 13, Article number: 416 (2022). [DOI] [Download]
  18. E. Moerman, F. Hummel, A. Grüneis, A. Irmler, M. Scheffler

    Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions

    Journal of Open Source Software (JOSS) 7 (74), 4040 (2022). [DOI] [Download]
  19. T. Purcell, M. Scheffler, L. M. Ghiringhelli, C. Carbogno

    Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity

    Preprint , (2022). [DOI] [Download]
  20. 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] [Download]
  21. 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, X. W. Yang

    DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science

    Submitted to Physical Chemistry Chemical Physics (June 2022) , (2022).  [Download]
  22. 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)

    Physical Review Letter 128, 246101 (2022). [DOI] [Download]
  23. 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 , (2022). [DOI] [Download]
  24. F. Knoop, M. Scheffler, and C. Carbogno

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

    Preprint , (2022). [DOI] [Download]
  25. F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno

    Anharmonicity in Thermal Insulators – An Analysis from First Principles

    Preprint , (2022). [DOI] [Download]
  26. B. Hoock, S. Rigamonti, and C. Draxl

    Advancing descriptor search in materials science: feature engineering and selection strategies

    submitted to New J. Phys. , (2022). [DOI] [Download]
  27. 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] [Download]
  28. 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] [Download]
  29.  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

    submitted to Model. Simul. Mat. Sci. Eng. , (2022). [DOI] [Download]
  30. 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

    Computer Physics Communications 267, 108045 (2021). [DOI] [Download]
  31. 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] [Download]
  32. 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] [Download]
  33. 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] [Download]
  34. 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

    Journal of Open Source Software (JOSS) 6 (65), 3458 (2021). [DOI] [Download]
  35. 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, (2021). [DOI] [Download]
  36. L. Foppa and L. M. Ghiringhelli

    Identifying outstanding transition-metal-alloy heterogeneous catalysts for the oxygen reduction and evolution reactions via subgroup discovery

    Topics in Catalysis, published online 02. September 2021 , (2021). [DOI] [Download]
  37. L. M. Ghiringhelli

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

    Nature Review Physics 3, 724 (2021). [DOI] [Download]
  38. 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

    Computational Materials Science 199, 110731 (2021). [DOI] [Download]
  39. 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 Materials 8, 044002 (2021). [DOI] [Download]
  40. S. Kokott, I. Hurtado, C. Vorwerk, C. Draxl, V. Blum, and M. Scheffler

    GIMS: Graphical Interface for Materials Simulations

    Journal of Open Source Software (JOSS) 6 (57), 2767 (2021). [DOI] [Download]
  41. A. Leitherer, A. Ziletti, and L.M. Ghiringhelli

    Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning

    Nature Communications 12, 6234 (2021). [DOI] [Download]
  42. 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 Computational Materials 7, 22 (2021). [DOI] [Download]
  43. 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. Materials 5, 013807 (2021). [DOI] [Download]
  44. 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] [Download]
  45. 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] [Download]