NOMAD Laboratory



F. Bertoldo, S. Ali, S. Manti, K. S. Thygesen 
Quantum point defects in 2D materials: The QPOD database.
Preprint download: arXiv

M. Boley and M. Scheffler
Learning Rules for Materials Properties and Functions.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Preprint Download: 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.
submitted to J. Phys.: Condens. Matter
Preprint Download: arXiv

J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov
Interpretable Machine Learning for Materials Design.
Preprint Download: arXiv

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.
In print in ACS Catalysis: chemrxiv

N. R. Knosgaard and K. S. Thygesen
Representing individual electronic states for machine learning GW band structures of 2D materials.
Preprint download: 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.
Nature Communications 13, 419 (2022)
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M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H.-J. Bungartz, C. Felser, M. Greiner, A. Groß, C. T. Koch, K. Kremer, W. E. Nagel, M. Scheidgen, C. Wöll, and C. Draxl
FAIR data – new horizons for materials research.
Nature Perspectives: accepted for publication in (Nov. 2021) 
Preprint download: pdf

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.
Download preprint: 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); https://doi.org/10.1038/s41597-021-00974-z
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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 6(65), 3458 (2021)
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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); https://doi.org/10.1557/s43577-021-00165-6
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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; https://doi.org/10.1007/s11244-021-01502-4
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L. M. Ghiringhelli 
An AI-toolkit to develop and share research into new materials.
Nature Review Physics 3, 724 (2021); https://doi.org/10.1038/s42254-021-00373-8
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L. M. Ghiringhelli
Interpretability of machine-learning models in physical sciences.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Preprint Download: arXiv
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); https://doi.org/10.1016/j.commatsci.2021.110731
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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; https://doi.org/10.11583/DTU.14616660
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S. Kokott, I. Hurtado, C. Vorwerk, C. Draxl, V. Blum, and M. Scheffler
GIMS: Graphical Interface for Materials Simulations. 
Journal of Open Source Software 6(57), 2767 (2021); https://doi.org/10.21105/joss.02767
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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); https://doi.org/10.1038/s41467-021-26511-5
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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); https://doi.org/10.1038/s41524-020-00480-7
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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); https://doi.org/10.1103/PhysRevMaterials.5.013807
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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, C. Draxl, and M. Scheffler
Roadmap: Organic-inorganic hybrid perovskite semiconductors and devices.
APL Materials 9, 109202 (2021); https://doi.org/10.1063/5.0047616
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F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno
FHI-vibes: Ab Initio Vibrational Simulations.
Journal of Open Source Software, 5(56) 2671 (2020); doi:10.21105/joss.02671
Download: pdf 
F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno
Anharmonicity Measure for Materials.
Phys. Rev. Materials 4, 083809 (2020)
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