Selected talks and publications


  1. F. A. Delesma, M. Leucke, D. Golze, and P. Rinke
    Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework
    J. Chem. Phys. 160, 024118 (2024). [DOI]
  2. A.S. Rosen, M. Gallant, J. George, J. Riebesell, H. Sahasrabuddhe, J.‑X. Shen, M. Wen, M.L. Evans, G. Petretto, D. Waroquiers, G.‑M. Rignanese, K.A. Persson, A. Jain, and A.M. Ganose
    Jobflow: Computational Workflows Made Simple
    J. Open Source Softw. 9, 5995 (2024). [DOI]
  3. I. Mas Magre, R. Grima Torres, J. M. Cela Espín, J. Gutierrez Moreno
    The NOMAD mini-apps: A suite of kernels from ab initio electronic structure codes enabling co-design in high-performance computing
    Awaiting Review , (2024). [DOI]
  4. S. Bi, C. Carbogno, I. Y. Zhang, M. Scheffler
    Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework
    J. Chem. Phys. 160, 034106 (2024). [DOI]
  5. P. Grigorev, L. Frérot, F. Birks, A. Gola, J. Golebiowski, J. Grießer, J. L. Hörmann, A. Klemenz, G. Moras, W. G. Nöhring, J. A. Oldenstaedt, P. Patel,  T. Reichenbach, T. Rocke, L. Shenoy, M. Walter, S. Wengert, L. Zhang, J. R. Kermode and L. Pastewka
    matscipy: materials science at the atomic scale with Python
    Journal of Open Source Science 9, 5668 (2024). [DOI]
  6. A. D. Fuchs, J. A. F. Lehmeyer, H. Junkes, H. B. Weber, and M. Krieger
    NOMAD CAMELS: Configurable Application for Measurements, Experiments and Laboratory Systems
    J. Open Source Softw. 9, 6371 (2024). [DOI]
  7. S. Kokott, F. Merz, Y. Yao, C. Carbogno, M. Rossi, M, Rampp, V. Havu, M. Scheffler, V. Blum
    Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms
    Preprint , (2024).  [arXiv]
  8. G. Wlazlowski, M. McNeil Forbes, S. R. Sarkar, A. Marek, M. Szpindler
    Fermionic Quantum Turbulence: Pushing the limits of High-Performance Computing
    PNAS Nexus , 160 (2024). [DOI]
  9. M. Baldovin, A. Browaeys, J.M. De Teresa, C. Draxl, F. Druon, F. Fradenigo, J.-J. Freffet, F. Lépine, J. Lüning, L. Reining, P. Salières, P. Seneor, L. Silva, T. Tschentscher, K. van Der Beek, A. Vollmer, and A. Vulpiani
    Matter and Waves, Chapter 3 in EPS Grand Challenges -  Physics for Society in the Horizon 2050
    IOP Publishing 1, 120 (2024). [DOI]
  10. M. Kuban, S. Rigamonti, C. Draxl
    MADAS: A Python framework for assessing similarity in materials-science data
    , 12 (2024). [DOI]
  11. S. Klawohn, J. R. Kermode, and A. P. Bartók
    Massively Parallel Fitting of Gaussian Approximation Potentials
    Mach. Learn. Sci. Tech. 4, 015020 (2023). [DOI]
  12. 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
    Sci. Data 10, 626 (2023). [DOI]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. Mehrdad Jalali, A.D. Dinga Wonanke, Christof Wöll
    MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks
    J. Cheminform. 15, 94 (2023). [DOI]
  24. M. Scheidgen, L. Himanen, A. N. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. A. Márquez, S. Brockhauser, S. Brückner, L. M. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. F. Rudzinski, N. Daelman, J. M. Pizarro, M. Kuban, C. Salazar, P. Ondračka, H.-J. Bungartz, and C. Draxl 
    NOMAD: A distributed web-based platform for managing materials science research data
    J. Open Source Softw. 8, 5388 (2023). [DOI]
  25. Clara Patricia Marshall, Julia Schumann, Anette Trunschke
    Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use
    Angew. Chem. Int. Ed 62, e202302971 (2023). [DOI]
  26. 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]
  27. 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]
  28. 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. 159, 174108 (2023). [DOI]
  29. 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]
  30. S. Lu, L. M. Ghiringhelli, C. Carbogno, J. Wang, M. Scheffler
    On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials
    Submitted for publication , (2023).  [arXiv]
  31. O. T. Beynon, A. Owens, C. Carbogno, and A. J. Logsdail
    On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials
    J. Phys. Chem. C 127, 16030 (2023). [DOI]
  32. R. Miyazaki, K. S. Belthle, H. Tüysüz, L. Foppa, M. Scheffler
    Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: an AI Approach Integrating Theoretical and Experimental Data.
    ChemRxiv. Cambridge: Cambridge Open Engage , (2023). [DOI]
  33. A. Leitherer, B. C. Yeo, C. H. Liebscher, and L. M. Ghiringhelli
    Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy
    npj Computational Materials 9, 17 (2023). [DOI]
  34. L. Foppa, M. Scheffler
    Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance
    Submitted for publication , (2023).  [arXiv]
  35. L. Foppa, F. Rüther, M. Geske, G. Koch, F. Girgsdies, P. Kube, S. J. Carey, M. Hävecker, O. Timpe, A. V. Tarasov, M. Scheffler, F. Rosowski, R. Schlögl, and A. Trunschke
    Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation
    J. Am. Chem. Soc. 145, 3427–3442 (2023). [DOI]
  36. J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov
    Interpretable Machine Learning for Materials Design
    Journal of Materials Research 38, 4477–4496 (2023). [DOI]
  37. S. Ali, F. Andreas Nilsson, S. Manti, F. Bertoldo, J. J. Mortensen, and K. S. Thygesen
    High-Throughput Search for Triplet Point Defects with Narrow Emission Lines in 2D Materials
    ACS Nano 17, 21105–21115 (2023). [DOI]
  38. A. Irmler, R. Kanakagiri, S.T. Ohlmann, E. Solomonik, A. Grüneis
    Optimizing Distributed Tensor Contractions Using Node-Aware Processor Grids
    In: Cano, J., Dikaiakos, M.D., Papadopoulos, G.A., Pericàs, M., Sakellariou, R. (eds) Euro-Par 2023: Parallel Processing. Lecture Notes in Computer Science , 14100, Springer, Cham. (2023). [DOI]
  39. J. Willis, R. Claes, Q. Zhou, M. Giantomassi, G.‑M. Rignanese, G. Hautier, and D.O. Scanlon
    Limits to Hole Mobility and Doping in Copper Iodide
    Chem. Mater. 35, 8995 (2023). [DOI]
  40. C. Tantardini, A. G. Kvashnin, M. Azizi, X. Gonze, C. Gatti, T. Altahi, B. I. Yakobson
    Electronic properties of functionalized diamanes for field emission displays
    ACS Applied Materials & Interfaces 15, 16317 (2023). [DOI]
  41. M. Boley, F. Luong, S. Teshuva, D. F. Schmidt, L. Foppa, M. Scheffler
    From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery
    Submitted , (2023).  [arXiv]
  42. R.L. Panadés-Barrueta and D. Golze
    Accelerating Core-Level GW Calculations by Combining the Contour Deformation Approach with the Analytic Continuation of W
    J. Chem. Theory Comput. 19, 16 (2023). [DOI]
  43. A. P. Bartók and J. R. Kermode
    Improved Uncertainty Quantification for Gaussian Process Regression Based Interatomic Potentials
    Preprint , (2022).  [arXiv]
  44. 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]
  45. 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]
  46. 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]
  47. 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]
  48. 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]
  49. J. P. Darby, J. R. Kermode, and G. Csányi
    Compressing Local Atomic Neighbourhood Descriptors
    npj Comput. Mater. 8, 166 (2022). [DOI] [arXiv]
  50. 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]
  51. 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]
  52. 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]
  53. 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]
  54. A. Gulans and C. Draxl
    Influence of spin-orbit coupling on chemical bonding
    Preprint , (2022).  [arXiv]
  55. 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]
  56. 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]
  57. 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]
  58. 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]
  59. 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]
  60. 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. 47, 28700 (2022). [DOI] [arXiv]
  61. 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]
  62. 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, 991 (2022). [DOI] [arXiv]
  63. Y. Luo, S. Bag, O. Zaremba, A. Cierpka, J. Andreo, S. Wuttke, P. Friederich, and M. Tsotsalas
    MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning
    Angew. Chem. Int. Ed. 61, e202200242 (2022). [DOI]
  64. M. Jalali, M.  Tsotsalas, and C. Wöll
    MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis
    Nanomaterials 12, 704 (2022). [DOI]
  65. 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]
  66. 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]
  67. 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]
  68. 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]
  69. 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]
  70. 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]
  71. 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]
  72. B. Regler, M. Scheffler, and L.M. Ghiringhelli,
    TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions.
    Data Min Knowl Disc 36, 1815–1864 (2022). [DOI]
  73. M. F. Langer, A. Goeßmann, and M. Rupp
    Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning.
    npj Computational Materials 8, 41 (2022). [DOI]
  74. V. Blum, M. Rossi, S. Kokott, and M. Scheffler
    The FHI-aims Code: All-electron, ab initio materials simulations towards the exascale
    Modelling and Simulation in Materials Science and Engineering 30, Preprint (2022).  [arXiv]
  75. 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]
  76. 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]
  77. 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]
  78. 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]
  79. 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]
  80. 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]
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