NOMAD Laboratory



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
Accepted at npj Computational Materials (2022)
Preprint Download: arXiv

J. Li, Y. Jin, P. Rinke, W. Yang, D. Golze
Benchmark of GW Methods for Core-Level Binding Energies
Preprint Download: arXiv

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 Download: arXiv

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 Download: arXiv

F. Bertoldo, S. Ali, S. Manti, K. S. Thygesen, 
Quantum point defects in 2D materials: The QPOD database
npj Comput Mater 8, 56 (2022); https://doi.org/10.48550/arXiv.2110.01961
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
Download: arXiv

J. P. Darby, J. R. Kermode and G. Csányi,
Compressing Local Atomic Neighbourhood Descriptors
Accepted at npj Computational Materials (2022).
Preprint Download: arXiv

C. Draxl, M. Kuban, S. Rigamonti, and M. Scheidgen,
Challenges and perspectives for interoperability and reuse of heterogeneous data collections
Section 4.1 in H. J. Kulik, et al.
Roadmap on Machine Learning in Electronic Structure (2022)
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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 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
ACS Catalysis 12, 2223 (2022); https://doi.org/10.1021/acscatal.1c04793ACS 
Download: ACS Publications

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
Download: arXiv

A. Gulans and C. Draxl,
Influence of spin-orbit coupling on chemical bonding
Preprint Download: arXiv

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); https://doi.org/10.1038/s41467-022-28122-0
Download: arXiv

M. Kuban, S. Rigamonti, M. Scheidgen, and C. Draxl,
Density-of-states similarity descriptor for unsupervised learning from materials data
Preprint Download: arXiv

M. Kuban, S. Gabaj, W. Aggoune, C. Vona, S. Rigamonti, and C. Draxl,
Similarity of materials and data-quality assessment by fingerprinting
MRS Bulletin Impact (2022)
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); https://doi.org/10.1038/s41467-022-28042-z 
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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
Submitted to Journal of Open Source Software (JOSS) (2022).
Preprint Download: arXiv

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 Download: 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); https://doi.org/10.48550/arXiv.2204.13240 
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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)
Preprint Download: ChemRxiv

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); https://doi.org/10.48550/arXiv.2202.01193
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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); https://doi.org/10.1016/j.cpc.2021.108045
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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
Download: Psi-k

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); https://doi.org/10.1063/5.0074936
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
J. Phys.: Condens. Matter 33, 404002 (2021); https://doi.org/10.1088/1361-648X/ac1280 
Download: 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); https://doi.org/10.21105/joss.03458
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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
Download: pdf
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
Download: pdf
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
Download: pdf
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); 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
Download: pdf
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
Download: pdf
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, M. Scheffler, and C. Draxl,
Roadmap: Organic-inorganic hybrid perovskite semiconductors and devices
APL Materials 9, 109202 (2021); https://doi.org/10.1063/5.0047616 
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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); https://doi.org/10.48550/arXiv.2006.01915
Download: arXiv