- 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] - 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] - 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
Under Review , (2024). [DOI] - 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] - 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] - 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
J. Chem. Phys. 161, 024112 (2024). [DOI] - G. Wlazlowski, M. McNeil Forbes, S. R. Sarkar, A. Marek, M. Szpindler
Fermionic Quantum Turbulence: Pushing the limits of High-Performance Computing
PNAS Nexus 3, 160 (2024). [DOI] - R. Rodrigues Pela, C. Vona, S. Lubeck, B. Alex, I. Gonzalez Oliva, and C. Draxl
Critical assessment of G0W0 calculations for 2D materials: the example of MoS2
npj Comp. Mater. 10, 77 (2024). [DOI] - I. Gonzalez Oliva, B. Maurer, B. Alex, S. Til-lack, M. Schebek, and C. Draxl
Hybrid materials: Still challenging for ab initio theory?
phys. stat. sol. (a) 221, 2300170 (2024). [DOI] - M. L. Evans, J. Bergsma, A. Merkys, C. W. Andersen, O. B. Andersson, D. Beltrán, E. Blokhin, T. M. Boland, R. Castañeda Balderas, K. Choudhary, A. Díaz Díaz, R. Domínguez García, H. Eckert, K. Eimre, M. E. Fuentes Montero, A. M. Krajewski, J. J. Mortensen, J. M. Nápoles Duarte, J. Pietryga, J. Qi, F. de Jesús Trejo Carrillo, A. Vaitkus, J. Yu, A. Zettel, P. B. de Castro, J. Carlsson, T. F. T. Cerqueira, S. Divilov, H. Hajiyani, F. Hanke, K. Jose, C. Oses, J. Riebesell, J. Schmidt, D. Winston, C. Xie, X. Yang, S. Bonella, S. Botti, S. Curtarolo, C. Draxl, L. E. Fuentes Cobas, A. Hospital, Z.-K. Liu, M. A. L. Marques, N. Marzari, A. J. Morris, S. P. Ong, M. Orozco, K. A. Persson, K. S. Thygesen, C. Wolverton, M. Scheidgen, C. Toher, G. J. Conduit, G. Pizzi, S. Gražulis, G.-M. Rignanese and R. Armiento
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Digital Discovery , (2024). [DOI] - P. Lyngby and K. S. Thygesen
Ab initio property characterisation of thousands of previously unknown 2D materials
2D Mater. 11, 035030 (2024). [DOI] - S. Americo, S. Pakdel, K. S. Thygesen
Enhancing Metallicity and Basal Plane Reactivity of 2D Materials via Self-Intercalation
ACS Nano 18, 4746–4755 (2024). [DOI] - S. Pakdel, A. Rasmussen, A. Taghizadeh, M. Kruse, T. Olsen & K. S. Thygesen
High-throughput computational stacking reveals emergent properties in natural van der Waals bilayers
Nat. Commun. 15, 932 (2024). [DOI] - S. Rigamonti, M. Troppenz, M. Kuban, A. Hübner, and C. Draxl
CELL: a Python package for cluster expansion with a focus on complex alloys
npj Comput Mater 10, 195 (2024). [DOI] - S. Klawohn, J. R. Kermode, and A. P. Bartók
Massively Parallel Fitting of Gaussian Approximation Potentials
Mach. Learn. Sci. Tech. 4, 015020 (2023). [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
Sci. Data 10, 626 (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] - 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] - 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
J. Chem. Phys. 159, 114110 (2023). [DOI] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - L. Foppa, M. Scheffler
Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance
Submitted for publication , (2023). [arXiv] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - 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] - J. Davidsson, F.Bertoldo, K. S. Thygesen, R. Armiento
Absorption versus adsorption: high-throughput computation of impurities in 2D materials
npj 2D Mater Appl 7, 26 (2023). [DOI] - S. Manti, M. Kamper Svendsen, N. R. Knøs-gaard, P. M. Lyngby, K. S. Thygesen
Exploring and machine learning structural instabilities in 2D materials
npj Comput Mater 9, 33 (2023). [DOI] - C. Vona, S, Lubeck, H. Kleine, A. Gulans, and C. Draxl
Accurate and efficient treatment of spin-orbit coupling via second variation employing local orbitals
Phys. Rev. B 108, 235161 (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] - 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 Functions
Section 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 collections
Section 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 sciences
Section 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. 47, 28700 (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 fingerprinting
MRS Bulletin Impact section
MRS Bulletin 47, 991 (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] - 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] - 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] - 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] - 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] - 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]