- S. Klawohn, J. R. Kermode, and A. P. Bartók
Massively Parallel Fitting of Gaussian Approximation Potentials Mach. Learn. Sci. Tech. 4, 015020 (2023). [DOI] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - A. P. Bartók and J. R. Kermode
Improved Uncertainty Quantification for Gaussian Process Regression Based Interatomic Potentials Preprint , (2022). [arXiv] - 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] - 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] - 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] - 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] - 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] - M. Boley and M. Scheffler
Learning Rules for Materials Properties and FunctionsSection 1.4 in H. J. Kulik, et al. Roadmap on Machine Learning in Electronic Structure
Electronic Structure 4, 023004 (2022). [DOI] - J. P. Darby, J. R. Kermode, and G. Csányi
Compressing Local Atomic Neighbourhood Descriptors npj Comput. Mater. 8, 166 (2022). [DOI] [arXiv] - C. Draxl, M. Kuban, S. Rigamonti, and M. Scheidgen
Challenges and perspectives for interoperability and reuse of heterogenous data collectionsSection 4.1 in H. J. Kulik, et al.
Electronic Structure 4, 023004 (2022). [DOI]
Roadmap on Machine Learning in Electronic Structure - 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] - 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] - L.M. Ghiringhelli
Interpretability of machine-learning models in physical sciencesSection 5.3 in H. J. Kulik, et al.
Electronic Structure 4, 023004 (2022). [DOI] [arXiv]
Roadmap on Machine Learning in Electronic Structure - A. Gulans and C. Draxl
Influence of spin-orbit coupling on chemical bonding Preprint , (2022). [arXiv] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - M. Kuban, Š. Gabaj, W. Aggoune, C. Vona, S. Rigamonti, and C. Draxl
Similarity of materials and data‑quality assessment by fingerprintingMRS Bulletin Impact section
MRS Bulletin 47, 1 (2022). [DOI] [arXiv] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - L. M. Ghiringhelli
An AI-toolkit to develop and share research into new materials Nat. Rev. Phys. 3, 724 (2021). [DOI] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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]