The NOMAD Laboratory

Novel Materials Discovery at the FHI Molecular Physics Department
of the Max-Planck-Gesellschaft


Dr. Luca Ghiringhelli


Member since 06/2009
Phone: +49 30 8413 4802
Room: T 1.08
Email: luca.ghiringhelli@fau.de


RESEARCH GROUP: Big-Data Analytics for Materials Science  




  1. S. Bauer, P. Benner, T. Bereau, V. Blum, M. Boley, C. Carbogno, C. R. A. Catlow, G. Dehm, S. Eibl, R. Ernstorfer, Á. Fekete, L. Foppa, P. Fratzl, C. Freysoldt, B. Gault, L. M. Ghiringhelli, S. K. Giri, A. Gladyshev, P. Goyal, J. Hattrick-Simpers, L. Kabalan, P. Karpov, M. S. Khorrami, C. Koch, S. Kokott, T. Kosch, I. Kowalec, K. Kremer, A. Leitherer, Y. Li, C. H. Liebscher, A. J. Logsdail, Z. Lu, F. Luong, A. Marek, F. Merz, J. R. Mianroodi, J. Neugebauer, T. A. R. Purcell, D. Raabe, M. Rampp, M. Rossi, J.-M. Rost, U. Saalmann, A. Saxena, L. Sbailo, M. Scheffler, M. Scheidgen, M. Schloz, D. F. Schmidt, S. Teshuva, A. Trunschke, Y. Wei, G. Weikum, R. P. Xian, Y. Yao, M. Zhao,
    Roadmap on Data-Centric Materials Science.
    accepted May 17, 2024 in Modelling Simul. Mater. Sci. Eng; https://doi.org/10.1088/1361-651X/ad4d0d
    Preprint Download (2024): pdf

  2. 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 September 1, 2023; https://doi.org/10.48550/arXiv.2309.00195
    Preprint Download (2023): arXiv



  1. 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.
    Scientific Data 10, 626 (2023); https://doi.org/10.1038/s41597-023-02501-8
    Download: pdf

  2. 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); https://doi.org/10.1038/s41524-023-01133-1
    Download: pdf

  3. 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. Rudzinski, N. Daelman, J. M. Pizarro, M. Kuban, P. Ondra─Źka, H.-J. Bungartz, and C. Draxl,
    NOMAD: A distributed web-based platform for managing materials science research data.
    Journal of Open Source Software, 8 (90), 5388; https://doi.org/10.21105/joss.05388
    Download: pdf

  4. T. A. R. 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.
    npj Computational Materials 9 (1), 112 (2023); https://doi.org/10.1038/s41524-023-01063-y
    Download: pdf

  5. 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); https://doi.org/10.1063/5.0156620
    Download (2023): pdf



  1. 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); https://doi.org/10.1038/s41524-022-00744-4
    Download: pdf

  2. 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 .
    Physical Review Letters 129, 55301 (2022); https://doi.org/10.1103/PhysRevLett.129.055301
    Download: pdf
  3. 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.1c04793
    Download: ACS Publications
  4. L. M. Ghiringhelli,
    Interpretability of machine-learning models in physical sciences.
    Roadmap on Machine learning in electronic structure, ed. by Silvana Botti and Miguel Marques,
    H J Kulik et al 2022 Electron. Struct. 4 023004, https://doi.org/10.1088/2516-1075/ac572f 
    Download: pdf

  5. 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
    Download: pdf

  6. 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); https://doi.org/10.1007/s10618-022-00847-y
    Download: pdf

  7. L. Sbailò, Á. Fekete, L. M. Ghiringhelli, and M. Scheffler
    The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding.
    npj Computational Materials 8, 250 (2022); https://doi.org/10.1038/s41524-022-00935-z
    Download: pdf

  8. T. A. R. Purcell, M. Scheffler, C. Carbogno, and L.M. Ghiringhelli,
    SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approach.
    Journal of Open Source Software 7 (71), 3960 (2022); https://doi.org/10.21105/joss.03960
    Download: pdf

  9. 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/10.1103/PhysRevLett.128.246101
    Download: pdf



  1. A. Dutta, J. Vreeken, L.M. Ghiringhelli and T. Bereau,
    Data-driven equation for drug-membrane permeability across drugs and membranes.
    The Journal of Chemical Physics 154 (24), 244114 (2021); https://doi.org/10.1063/5.0053931
    Download: pdf
  2. A. Dutta, J. Vreeken, L.M. Ghiringhelli and T. Bereau,
    Publisher’s Note: “Data-driven equation for drug-membrane permeability across drugs and membranes.”
    J. J. chem. Phys. 154, 244114 (2021)]. The Journal of Chemical Physics 155 (3), 039901 (2021); https://doi.org/10.1063/5.0061875
    Download: pdf
  3. 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
  4. 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
    Download: pdf
  5. 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
  6. 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
  7. A. Mazheika, S.V. Levchenko, L.M. Ghiringhelli and M. Scheffler,
    Materials for turning greenhouse gases into useful chemicals and fuels: a route identified by high-throughput calculations and artificial intelligence.
    In: High-Performance Computing and Data Science in the Max Planck Society. Max Planck Computing and Data Facility, Garching, 44–46 (2021).
    Download: pdf
  8. L. Talirz, L.M. Ghiringhelli and B. Smit,
    Trends in Atomistic Simulation Software Usage.
    [Articlev1.0]. Living Journal of Computational Molecular Science 3 (1), 1–12 (2021); https://doi.org/10.33011/livecoms.3.1.1483
    Download: pdf



  1. G. Cao, R. Ouyang, L.M. Ghiringhelli, M. Scheffler, H. Liu, C. Carbogno, and Z. Zhang,
    Artificial Intelligence for High-Throughput Discovery of Topological Insulators: The Example of Alloyed Tetradymites. Phys. Rev. Mater. 4, 034204 (2020); https://doi.org/10.1103/PhysRevMaterials.4.034204
    Reprint download: pdf, Arxiv
  2. A. Dutta, J. Vreeken, L. M. Ghiringhelli, and T. Bereau,
    Data-driven equation for drug-membrane permeability across drugs and membranes. J. Chem. Phys. 154, 244114 (2021); https://doi.org/10.1063/5.0053931
    Reprint download: pdf
  3. C. Sutton, M. Boley, L.M. Ghiringhelli, M. Rupp, J. Vreeken, and M. Scheffler,
    Identifying domains of applicability of machine learning models for materials science. Nat. Commun. 11, 4428 (2020); https://doi.org/10.1038/s41467-020-17112-9 
    Reprint download: pdf

  4. A. Trunschke, G. Bellini, M. Boniface, S. J. Carey, J. Dong, E. Erdem, L. Foppa, W. Frandsen, M. Geske, L. M. Ghiringhelli, F. Girgsdies, R. Hanna, M. Hashagen, M. Hävecker, G. Huff, A. Knop-Gericke, G. Koch, P. Kraus, J. Kröhnert, P. Kube, S. Lohr, T. Lunkenbein, L. Masliuk, R. Naumann d’Alnoncourt, T. Omojola, Ch. Pratsch, S. Richter, C. Rohner, F. Rosowski, F. Rüther, M. Scheffler, R. Schlögl, A. Tarasov, D. Teschner, O. Timpe, P. Trunschke, Y. Wang, and S. Wrabetz,
    Towards Experimental Handbooks in Catalysis. Topics in Catalysis 63, 1683 (2020); https://doi.org/10.1007/s11244-020-01380-2
    Reprint download: pdf
  5. C. Wouters, C. Sutton, L. M. Ghiringhelli, T. Markurt, R. Schewski, A. Hassa, H. von Wenckstern, M. Grundmann, M. Scheffler, and M. Albrecht,
    Investigating the ranges of (meta)stable phase formation in (InxGa1-x)2O3: Impact of the cation coordination. Phys. Rev. Materials 4, 125001 (2020); https://doi.org/10.1103/PhysRevMaterials.4.125001
    Reprint download: pdf,



  1. C.J. Bartel, C. Sutton, B.R. Goldsmith, R. Ouyang, C.B. Musgrave, L.M. Ghiringhelli, and M. Scheffler,
    New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides. Sci. Adv. 5, eaav0693 (2019); https://doi.org/10.1126/sciadv.aav0693
    Reprint download: pdf, Supplementary material: pdf
  2. F. Belviso, V.E.P. Claerbout, A. Comas-Vives, N.S. Dalal, F.-R. Fan, A. Filippetti, V. Fiorentini, L. Foppa, C. Franchini, B. Geisler, L.M. Ghiringhelli, A. Groß, S. Hu, J. Íñiguez, S.K. Kauwe, J.L. Musfeldt, P. Nicolini, R. Pentcheva, T. Polcar, W. Ren, F. Ricci, F. Ricci, H.S. Sen, J.M. Skelton, T.D. Sparks, A. Stroppa, A. Urru, M. Vandichel, P. Vavassori, H. Wu, K. Yang, H.J. Zhao, D. Puggioni, R. Cortese and A. Cammarata,
    Viewpoint: Atomic-Scale Design Protocols toward Energy, Electronic, Catalysis, and Sensing Applications. Inorganic Chemistry 58 (22), 14939 (2019); https://doi.org/10.1021/acs.inorgchem.9b01785
    Reprint download: pdf

  3. B.R. Goldsmith, J. Florian, J.-X. Liu, Ph. Gruene, J.T. Lyon, D.M. Rayner, A. Fielicke, M. Scheffler, and L.M. Ghiringhelli,
    Two-to-three dimensional transition in neutral gold clusters: The crucial role of van der Waals interactions and temperature. Phys. Rev. Mat. 3, 016002 (2019); https://doi.org/10.1103/PhysRevMaterials.3.016002
    Reprint download: pdf
  4. D. Guedes-Sobrinho, W. Wang, I. Hamilton, J.L.F. Da Silva, and L.M. Ghiringhelli,
    (Meta-)stability and Core-Shell Dynamics of Gold Nanoclusters at Finite Temperature. J. Phys. Chem. Lett. 10, 685-692 (2019); https://doi.org/10.1021/acs.jpclett.8b03397
    Reprint download: pdf, Supplementary material: pdf
  5. R. Ouyang, E. Ahmetcik, C. Carbogno, M. Scheffler, and L. M. Ghiringhelli,
    Simultaneous Learning of Several Materials Properties from Incomplete Databases with Multi-Task SISSO. J. Phys. Mater. 2, 024002 (2019);  https://doi.org/10.1088/2515-7639/ab077b

  6. C. Sutton, L.M. Ghiringhelli, T. Yamamoto, Y. Lysogorskiy, L. Blumenthal, T. Hammerschmidt, J. Golebiowski, X. Liu, A. Ziletti, and M. Scheffler,
    Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition. npj Computational Materials 5, 111 (2019);  https://doi.org/10.1038/s41524-019-0239-3

  7. I.-Y. Zhang, A.J. Logsdail, X. Ren, S.V. Levchenko, L.M. Ghiringhelli, and M. Scheffler,
    Main-group test set for materials science and engineering with user-friendly graphical tools for error analysis: Systematic benchmark of the numerical and intrinsic errors in state-of-the-art electronic-structure approximations. New J. Phys. 21, 013025 (2019);  https://doi.org/10.1088/1367-2630/aaf751

  8. Y. Zhou, M. Scheffler, and L.M.Ghiringhelli,
    Determining Surface Phase Diagrams Including Anharmonic Effects. Phys. Rev. B 100, 174106 (2019);  https://doi.org/10.1103/PhysRevB.100.174106



  1. R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, and L.M. Ghiringhelli,
    SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates . Phys. Rev. Mat. 2, 083802 (2018); https://doi.org/10.1103/PhysRevMaterials.2.083802
    Reprint download: pdf, Supplementary material: pdf

  2. A. Ziletti, D. Kumar, M. Scheffler, and L.M. Ghiringhelli,
    Insightful classification of crystal structures using deep learning. Nat. Commun. 9 , 2775 (2018); https:/doi.org/10.1038/s41467-018-05169-6



  1. S. Bhattacharya, D. Berger, K. Reuter, L.M. Ghiringhelli, and S.V. Levchenko,
    Theoretical evidence for unexpected O-rich phases at corners of MgO surfaces. Phys. Rev. Materials 1, 071601(R) (2017) .
    Reprint download: pdf, Supplementary material: pdf DOI: 10.1103/PhysRevMaterials.1.071601.
  2. M. Boley, B.R. Goldsmith, L.M. Ghiringhelli, and J. Vreeken,
    Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery. Data Min. Knowl. Disc. 31, 1391 (2017).
    Reprint download: pdf DOI: 10.1007/s10618-017-0520-3.
  3. L.M. Ghiringhelli, C. Carbogno, S.V. Levchenko, F. Mohamed, G. Huhs, M. Lueders, M. Oliveira, and M. Scheffler,
    Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats. npj Computational Materials 3, 46 (2017).
    Reprint download: pdf, Supplementary material: pdf DOI: 10.1038/s41524-017-0048-5.
  4. L.M. Ghiringhelli, J. Vybiral, E. Ahmetcik, R. Ouyang, S.V. Levchenko, C. Draxl, and M. Scheffler,
    Learning physical descriptors for materials science by compressed sensing. New J. Phys. 19, 023017 (2017).
    Reprint download: pdf DOI: 10.1088/1367-2630/aa57bf.
  5. B.R. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, and L.M. Ghiringhelli,
    Uncovering structure-property relationships of materials by subgroup discovery. New J. Phys. 19, 013031 (2017).
    Reprint download: pdf, Supplementary material: pdf DOI: 10.1088/1367-2630/aa57c2.
  6. C. Yu, W. Harbich, L. Sementa, L. Ghiringhelli, E. Aprá, M. Stener, A. Fortunelli, and H. Brune,
    Intense fluorescence of Au20. J. Chem. Phys. 147, 074301 (2017).
    Reprint download: pdf



  1. L.M. Ghiringhelli, C. Carbogno, S. Levchenko, F. Mohamed, G. Huhs, M. Lueders, M. Oliveira, and M. Scheffler,
    Towards a Common Format for Computational Materials Science Data. Published as "Ψk Scientific Highlight of the Month", n. 131 (July 2016).
    Reprint download: pdf



  1. S. Bhattacharya, B. Sonin, C.J. Jumonville, L.M. Ghiringhelli, and N. Marom,
    Computational Design of Nanoclusters by Property-Based Genetic Algorithms: Tuning the Electronic Properties of (TiO2)n Clusters. Phys. Rev. B 91, 241115 (2015).
    Reprint download: pdf, Supplementary material: pdf
  2. L.M. Ghiringhelli and S.V. Levchenko,
    Strengthening gold-gold bonds by complexing gold clusters with noble gases. Inor. Chem. Comm. 55, 153-156 (2015).
    Reprint download: pdf, Supplementary material: pdf
  3. L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, and M. Scheffler,
    Big Data of Materials Science: Critical Role of the Descriptor. Phys. Rev. Lett. 114, 105503 (2015).
    Reprint download: pdf, Supplementary material: pdf
  4. X. Zhao, X. Shao, Y. Fujimori, S. Bhattacharya, L.M. Ghiringhelli, H.-J. Freund, M. Sterrer, N. Nilius, and S.V. Levchenko,
    Formation of Water Chains on CaO(001): What Drives the 1D Growth? J. Phys. Chem. Lett. 6, 1204-1208 (2015).
    Reprint download: pdf



  1. E.C. Beret, M. van Wijk, and L.M. Ghiringhelli,
    Reaction cycles and poisoning in catalysis by gold clusters: a thermodynamics approach. Int. J. Quant. Chem. 114, 57-65 (2014).
    Reprint download: pdf
  2. S. Bhattacharya, S. Levchenko, L.M. Ghiringhelli and M. Scheffler,
    Efficient ab initio schemes for finding thermodynamically stable and metastable atomic structures: Benchmark of cascade genetic algorithms. New J. Phys. 16, 123016 (2014).
    Reprint download: pdf
  3. L.M. Ghiringhelli,
    Application to (Kohn-Sham) Density-Functional Theory to Real Materials.
    In Many-Electron Approaches in Physics, Chemistry and Mathematics: A Multidisciplinary View., V. Bach and L. Delle Site Editors, Springer (2014).
    Preprint download: pdf
  4. R. Peköz, S. Wörner, L.M. Ghiringhelli, and D. Donadio,
    Trends in the Adsorption and Dissociation of Water Clusters on Flat and Stepped Metallic Surfaces. J. Phys. Chem. C 118, 29990-29998 (2014).
    Reprint download: pdf
  5. Y. Peng, L.M. Ghiringhelli, and H. Appel,
    A quantum reactive scattering perspective on electronic nonadiabaticity. Eur. Phys. J. B 87, 1-13 (2014).
    Reprint download: pdf



  1. S. Bhattacharya, S. Levchenko, L.M. Ghiringhelli, and M. Scheffler,
    Stability and Metastability of Clusters in a Reactive Atmosphere: Theoretical Evidence for Unexpected Stoichiometries of MgMOx. Phys. Rev. Lett. 111, 135501 (2013).
    Reprint download: pdf, Supplementary material: pdf
  2. L. Delle Site, L.M. Ghiringhelli, and D.M. Ceperley
    Electronic Energy Functionals: Levy-Lieb Principle within the Ground State Path Integral Quantum Monte Carlo. Int. J. Quant. Chem. 113, 155-160 (2013).
    Reprint download: pdf.
  3. L.M. Ghiringhelli, P. Gruene, J.T. Lyon, D.M. Rayner, G. Meijer, A. Fielicke, and M. Scheffler,
    Not so loosely bound rare gas atoms: finite-temperature vibrational fingerprints of neutral gold-cluster complexes. New J. Phys. 15, 083003 (2013).
    Reprint download: pdf



  1. D. Donadio, L.M. Ghiringhelli, and L. Delle Site,
    Autocatalytic and Cooperatively Stabilized Dissociation of Water on a Stepped Platinum Surface. J. Am. Chem. Soc. 134, 19217-19222 (2012).
    Reprint download: pdf



  1. E.C. Beret, L.M. Ghiringhelli, and M. Scheffler,
    Free gold clusters: Beyond the static, mono-structure description. Faraday Discuss. 152 (1), 153-167 (2011).
    Reprint download: pdf
  2. J.M. Carlsson, L.M. Ghiringhelli, and A. Fasolino,
    Theory and hierarchical calculations of the structure and energetics of [0001] tilt grain boundaries in graphene. Phys. Rev. B 84, 165423 (2011).
    Reprint download: pdf
  3. R. Scipioni, D. Donadio, L.M. Ghiringhelli, and L. Delle Site,
    Proton Wires via One-Dimensional Water Chains Adsorbed on Metallic Steps. J. Chem. Theory and Comp. 7, 2681-2684 (2011).
    Reprint download: pdf



  1. L.M. Ghiringhelli and E.J. Meijer,
    "Liquid Carbon: Freezing Line and Structure near Freezing". In: Computer-based modeling of novel carbon systems (other than nanotubes) and their properties. (Eds.) A. Fasolino and L. Colombo. Springer 2010, p. 1-36.
    Reprint download: pdf



  1. T. Li, D. Donadio, L.M. Ghiringhelli, and G. Galli,
    "Surface-induced crystallization in supercooled tetrahedral liquids". Nature Materials 8, 726-730 (2009).
    Reprint download: pdf

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