Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 4,464

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

The atomic simulation environment—a Python library for working with atoms

Open access
Hjorth Larsen, Ask, Jørgen Mortensen, Jens, Blomqvist, Jakob, Castelli, Ivano E, Christensen, Rune, Dułak, Marcin, Friis, Jesper, Groves, Michael N, Hammer, Bjørk, Hargus, Cory, Hermes, Eric D, Jennings, Paul C, Bjerre Jensen, Peter, Kermode, James, Kitchin, John R, Leonhard Kolsbjerg, Esben, Kubal, Joseph, Kaasbjerg, Kristen, Lysgaard, Steen, Bergmann Maronsson, Jón, Maxson, Tristan, Olsen, Thomas, Pastewka, Lars, Peterson, Andrew, Rostgaard, Carsten, Schiøtz, Jakob, Schütt, Ole, Strange, Mikkel, Thygesen, Kristian S, Vegge, Tejs, Vilhelmsen, Lasse, Walter, Michael, Zeng, Zhenhua, Jacobsen, Karsten W·2017·Journal of Physics: Condensed Matter

Abstract

The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1088/1361-648x/aa680e

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 115

  1. Jones, 2015, Density functional theory: its origins, rise to prominence and future, Rev. Mod. Phys., 87, 897, 10.1103/RevModPhys.87.897 · DOI · Paper page
  2. Dubois, 1994, Making applications programmable, Comput. Phys., 8, 70, 10.1063/1.4823265 · DOI
  3. Bahn, 2002, An object-oriented scripting interface to a legacy electronic structure code, Comput. Sci. Eng., 4, 56, 10.1109/5992.998641 · DOI · Paper page
  4. Mills, 1994, Quantum and thermal effects in H2 dissociative adsorption: evaluation of free energy barriers in multidimensional quantum systems, Phys. Rev. Lett., 72, 1124, 10.1103/PhysRevLett.72.1124 · DOI · Paper page
  5. Jónsson, 1998, Nudged elastic band method for finding minimum energy paths of transitions, 385, 10.1142/9789812839664_0016 · DOI
  6. Khorshidi, 2016, Amp: a modular approach to machine learning in atomistic simulations, Comput. Phys. Commun., 207, 310, 10.1016/j.cpc.2016.05.010 · DOI
  7. Millman, 2011, Python for scientists and engineers, Comput. Sci. Eng., 13, 9, 10.1109/MCSE.2011.36 · DOI · Paper page
  8. Walt, 2011, The numpy array: a structure for efficient numerical computation, Comput. Sci. Eng., 13, 22, 10.1109/MCSE.2011.37 · DOI · Paper page
  9. Kendall, 2000, High performance computational chemistry: an overview of NWChem a distributed parallel application, Comput. Phys. Commun., 128, 260, 10.1016/S0010-4655(00)00065-5 · DOI
  10. Valiev, 2010, NWChem: a comprehensive and scalable open-source solution for large scale molecular simulations, Comput. Phys. Commun., 181, 1477, 10.1016/j.cpc.2010.04.018 · DOI
  11. Mortensen, 2005, Real-space grid implementation of the projector augmented wave method, Phys. Rev. B, 71, 10.1103/PhysRevB.71.035109 · DOI · Paper page
  12. Enkovaara, 2010, Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method, J. Phys.: Condens. Matter, 22, 10.1088/0953-8984/22/25/253202 · DOI
  13. Gonze, 2009, ABINIT: First-principles approach to material and nanosystem properties, Comput. Phys. Commun., 180, 2582, 10.1016/j.cpc.2009.07.007 · DOI · Paper page
  14. Clark, 2005, First principles methods using CASTEP, Z. Kristallogr.—Cryst. Mater., 220, 567, 10.1524/zkri.220.5.567.65075 · DOI · Paper page
  15. Hutter, 2014, CP2K: atomistic simulations of condensed matter systems, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 4, 15, 10.1002/wcms.1159 · DOI
  16. Aradi, 2007, DFTB+, a sparse matrix-based implementation of the DFTB method, J. Phys. Chem. A, 111, 5678, 10.1021/jp070186p · DOI
  17. Daw, 1983, Semiempirical, quantum mechanical calculation of hydrogen embrittlement in metals, Phys. Rev. Lett., 50, 1285, 10.1103/PhysRevLett.50.1285 · DOI · Paper page
  18. Jacobsen, 1996, A semi-empirical effective medium theory for metals and alloys, Surf. Sci., 366, 394, 10.1016/0039-6028(96)00816-3 · DOI
  19. Gulans, 2014, Exciting: a full-potential all-electron package implementing density-functional theory and many-body perturbation theory, J. Phys.: Condens. Matter, 26, 10.1088/0953-8984/26/36/363202 · DOI
  20. Blum, 2009, Ab initio molecular simulations with numeric atom-centered orbitals, Comput. Phys. Commun., 180, 2175, 10.1016/j.cpc.2009.06.022 · DOI
  21. Wimmer, 1981, Full-potential self-consistent linearized-augmented-plane-wave method for calculating the electronic structure of molecules and surfaces: O2 molecule, Phys. Rev. B, 24, 864, 10.1103/PhysRevB.24.864 · DOI · Paper page
  22. Pronk, 2013, GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit, Bioinformatics, 29, 845, 10.1093/bioinformatics/btt055 · DOI
  23. Koskinen, 2009, Density-functional tight-binding for beginners, Comput. Mater. Sci., 47, 237, 10.1016/j.commatsci.2009.07.013 · DOI · Paper page
  24. Ismail-Beigi, 2000, New algebraic formulation of density functional calculation, Comput. Phys. Commun., 128, 1, 10.1016/S0010-4655(00)00072-2 · DOI
  25. Jones, 1924, On the determination of molecular fields. II. From crystal measurements and kinetic theory data, 106, 709, 10.1098/rspa.1924.0098 · DOI
  26. Stewart, 1990, MOPAC: A semiempirical molecular orbital program, J. Comput.-Aided Mol. Des., 4, 1, 10.1007/BF00128336 · DOI
  27. Morse, 1929, Diatomic molecules according to the wave mechanics. II. Vibrational levels, Phys. Rev., 34, 57, 10.1103/PhysRev.34.57 · DOI
  28. Andrade, 2015, Real-space grids and the Octopus code as tools for the development of new simulation approaches for electronic systems, Phys. Chem. Chem. Phys., 17, 31371, 10.1039/C5CP00351B · DOI · Paper page
  29. Tadmor, 2011, The potential of atomistic simulations and the knowledgebase of interatomic models, JOM, 63, 17, 10.1007/s11837-011-0102-6 · DOI
  30. Giannozzi, 2009, QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials, J. Phys.: Condens. Matter, 21, 10.1088/0953-8984/21/39/395502 · DOI · Paper page
  31. Soler, 2002, The SIESTA method for ab initio order-N materials simulation, J. Phys.: Condens. Matter, 14, 2745, 10.1088/0953-8984/14/11/302 · DOI · Paper page
  32. Jorgensen, 1983, Comparison of simple potential functions for simulating liquid water, J. Chem. Phys., 79, 926, 10.1063/1.445869 · DOI · Paper page
  33. Furche, 2014, Turbomole, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 4, 91, 10.1002/wcms.1162 · DOI · Paper page
  34. Grimme, 2010, A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu, J. Chem. Phys., 132, 10.1063/1.3382344 · DOI · Paper page
  35. Haunschild, 2012, New accurate reference energies for the G2/97 test set, J. Chem. Phys., 136, 10.1063/1.4704796 · DOI
  36. Tripa, 1999, Molecular N2 chemisorption-specific adsorption on step defect sites on Pt surfaces, J. Chem. Phys., 111, 8651, 10.1063/1.480204 · DOI
  37. Freund, 2008, Oxide ultra-thin films on metals: new materials for the design of supported metal catalysts, Chem. Soc. Rev., 37, 2224, 10.1039/b718768h · DOI · Paper page
  38. Xie, 2014, Atomic layer-by-layer deposition of Pt on Pd nanocubes for catalysts with enhanced activity and durability toward oxygen reduction atomic layer-by-layer deposition of Pt on Pd nanocubes for catalysts with enhanced activity and durability toward oxygen, Nano Lett., 14, 3570, 10.1021/nl501205j · DOI
  39. Alchagirov, 2003, Reply to ‘Comment on ‘Energy and pressure versus volume: equations of state motivated by the stabilized jellium model’’, Phys. Rev. B, 67, 10.1103/PhysRevB.67.026103 · DOI
  40. Yu, 2004, Fast and scalable startup of MPI programs in InfiniBand clusters, 440, 10.1007/978-3-540-30474-6_47 · DOI
  41. Berendsen, 1984, Molecular dynamics with coupling to an external bath, J. Chem. Phys., 81, 3684, 10.1063/1.448118 · DOI · Paper page
  42. Hoover, 1985, Canonical dynamics—equilibrium phase-space distributions, Phys. Rev. A, 31, 1695, 10.1103/PhysRevA.31.1695 · DOI · Paper page
  43. Hoover, 1986, Constant-pressure equations of motion, Phys. Rev. A, 34, 2499, 10.1103/PhysRevA.34.2499 · DOI · Paper page
  44. Parrinello, 1980, Crystal-structure and pair potentials—a molecular-dynamics study, Phys. Rev. Lett., 45, 1196, 10.1103/PhysRevLett.45.1196 · DOI
  45. Parrinello, 1981, Polymorphic transitions in single-crystals—a new molecular-dynamics method, J. Appl. Phys., 52, 7182, 10.1063/1.328693 · DOI · Paper page
  46. Melchionna, 2000, Erratum: Constrained systems and statistical distribution (2000 Phys. Rev. E 61 6165), Phys. Rev. E, 62, 5864, 10.1103/PhysRevE.62.5864 · DOI
  47. Melchionna, 2000, Constrained systems and statistical distribution, Phys. Rev. E, 61, 6165, 10.1103/PhysRevE.61.6165 · DOI
  48. Press, 1992
  49. Nocedal, 1980, Updating quasi-Newton matrices with limited storage, Math. Comput., 35, 773, 10.1090/S0025-5718-1980-0572855-7 · DOI
  50. Liu, 1989, On the limited memory bfgs method for large scale optimization, Math. Prog., 45, 503, 10.1007/BF01589116 · DOI
  51. Bitzek, 2006, Structural relaxation made simple, Phys. Rev. Lett., 97, 10.1103/PhysRevLett.97.170201 · DOI · Paper page
  52. Packwood, 2016, A universal preconditioner for simulating condensed phase materials, J. Chem. Phys., 144, 10.1063/1.4947024 · DOI
  53. Peterson, 2014, Global optimization of adsorbate—surface structures while preserving molecular identity, Top. Catal., 57, 40, 10.1007/s11244-013-0161-8 · DOI
  54. Henkelman, 2000, Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points, J. Chem. Phys., 113, 9978, 10.1063/1.1323224 · DOI · Paper page
  55. Henkelman, 2000, A climbing image nudged elastic band method for finding saddle points and minimum energy paths, J. Chem. Phys., 113, 9901, 10.1063/1.1329672 · DOI · Paper page
  56. Smidstrup, 2014, Improved initial guess for minimum energy path calculations, J. Chem. Phys., 140, 10.1063/1.4878664 · DOI · Paper page
  57. Kolsbjerg, 2016, An automated nudged elastic band method, J. Chem. Phys., 145, 10.1063/1.4961868 · DOI
  58. Rasmussen, 2014, Remote activation of chemical bonds in heterogeneous catalysis, ACS Catal., 4, 1182, 10.1021/cs400875k · DOI
  59. Goubert, 2014, Walking-like diffusion of two-footed asymmetric aromatic adsorbates on Pt(1 1 1), Surf. Sci., 629, 123, 10.1016/j.susc.2014.03.021 · DOI
  60. Kolsbjerg, 2016, Pyridine adsorption and diffusion on Pt(1 1 1) investigated with density functional theory, J. Chem. Phys., 144, 10.1063/1.4947225 · DOI
  61. Melander, 2015, Removing external degrees of freedom from transition-state search methods using quaternions, J. Chem. Theory Comput., 11, 1055, 10.1021/ct501155k · DOI
  62. Pedersen, 2011, Efficient sampling of saddle points with the minimum-mode following method, SIAM J. Sci. Comput., 33, 633, 10.1137/100792743 · DOI
  63. Wales, 1997, Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms, J. Phys. Chem. A, 101, 5111, 10.1021/jp970984n · DOI
  64. Goedecker, 2004, Minima hopping: an efficient search method for the global minimum of the potential energy surface of complex molecular systems, J. Chem. Phys., 120, 9911, 10.1063/1.1724816 · DOI
  65. Forrest, 1993, Genetic algorithms: principles of natural selection applied to computation, Science, 261, 872, 10.1126/science.8346439 · DOI
  66. Johnston, 2003, Evolving better nanoparticles: genetic algorithms for optimising cluster geometries, Dalton Trans., 22, 4193, 10.1039/b305686d · DOI
  67. Glass, 2006, USPEX—Evolutionary crystal structure prediction, Comput. Phys. Commun., 175, 713, 10.1016/j.cpc.2006.07.020 · DOI · Paper page
  68. Jóhannesson, 2002, Combined electronic structure and evolutionary search approach to materials design, Phys. Rev. Lett., 88, 10.1103/PhysRevLett.88.255506 · DOI · Paper page
  69. Lysgaard, 2014, Genetic algorithm procreation operators for alloy nanoparticle catalysts, Top. Catal., 57, 33, 10.1007/s11244-013-0160-9 · DOI
  70. Lysgaard, 2015, A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO2 reduction, Phys. Chem. Chem. Phys., 17, 28270, 10.1039/C5CP00298B · DOI
  71. Vilhelmsen, 2012, Systematic study of Au6 to Au12 gold clusters on MgO(1 0 0) F centers using density-functional theory, Phys. Rev. Lett., 108, 10.1103/PhysRevLett.108.126101 · DOI
  72. Deaven, 1995, Molecular-geometry optimization with a genetic algorithm, Phys. Rev. Lett., 75, 288, 10.1103/PhysRevLett.75.288 · DOI · Paper page
  73. Vilhelmsen, 2014, A genetic algorithm for first principles global structure optimization of supported nano structures, J. Chem. Phys., 141, 10.1063/1.4886337 · DOI
  74. Jensen, 2014, Designing mixed metal halide ammines for ammonia storage using density functional theory and genetic algorithms, Phys. Chem. Chem. Phys., 16, 19732, 10.1039/C4CP03133D · DOI
  75. Jensen, 2015, Accelerated DFT-based design of materials for ammonia storage, Chem. Mater., 27, 4552, 10.1021/acs.chemmater.5b00446 · DOI
  76. Frederiksen, 2007, Inelastic transport theory from first-principles: methodology and applications for nanoscale devices, Phys. Rev. B, 75, 10.1103/PhysRevB.75.205413 · DOI
  77. Porezag, 1996, Infrared intensities and raman-scattering activities within density-functional theory, Phys. Rev. B, 54, 7830, 10.1103/PhysRevB.54.7830 · DOI · Paper page
  78. Profeta, 2001, Theory of resonant raman scattering of tetrahedral amorphous carbon, Phys. Rev. B, 63, 10.1103/PhysRevB.63.245415 · DOI
  79. Dove, 1993, 10.1017/CBO9780511619885 · DOI · Paper page
  80. Alf, 2009, PHON: A program to calculate phonons using the small displacement method, Comput. Phys. Commun., 180, 2622, 10.1016/j.cpc.2009.03.010 · DOI · Paper page
  81. Baroni, 1987, Green’s-function approach to linear response in solids, Phys. Rev. Lett., 58, 1861, 10.1103/PhysRevLett.58.1861 · DOI · Paper page
  82. Jones, 2008, First principles calculations and experimental insight into methane steam reforming over transition metal catalysts, J. Catal., 259, 147, 10.1016/j.jcat.2008.08.003 · DOI · Paper page
  83. Verink, 2011, 10.1002/9780470872864.ch7 · DOI
  84. Johnson, 1992, SUPCRT92: A software package for calculating the standard molal thermodynamic properties of minerals, gases, aqueous species and reactions from 1 to 5000 bar and 0 to 1000 °C, Comput. Geosci., 18, 899, 10.1016/0098-3004(92)90029-Q · DOI · Paper page
  85. Persson, 2012, Prediction of solid–aqueous equilibria: Scheme to combine first-principles calculations of solids with experimental aqueous states, Phys. Rev. B, 85, 10.1103/PhysRevB.85.235438 · DOI
  86. Castelli, 2013, Calculated pourbaix diagrams of cubic perovskites for water splitting: stability against corrosion, Top. Catal., 57, 265, 10.1007/s11244-013-0181-4 · DOI
  87. Castelli, 2015, New light-harvesting materials using accurate and efficient bandgap calculations, Adv. Energy Mater., 5, 1400915, 10.1002/aenm.201400915 · DOI
  88. Tersoff, 1985, Theory of the scanning tunneling microscope, Phys. Rev. B, 31, 805, 10.1103/PhysRevB.31.805 · DOI · Paper page
  89. Setyawan, 2010, High-throughput electronic band structure calculations: challenges and tools, Comput. Mater. Sci., 49, 299, 10.1016/j.commatsci.2010.05.010 · DOI · Paper page
  90. Mortensen, 2005, Bayesian error estimation in density-functional theory, Phys. Rev. Lett., 95, 10.1103/PhysRevLett.95.216401 · DOI · Paper page
  91. Wellendorff, 2012, Density functionals for surface science: Exchange–correlation model development with Bayesian error estimation, Phys. Rev. B, 85, 10.1103/PhysRevB.85.235149 · DOI
  92. Wellendorff, 2014, mBEEF: An accurate semi-local Bayesian error estimation density functional, J. Chem. Phys., 140, 10.1063/1.4870397 · DOI
  93. Wellendorff, 2015, A benchmark database for adsorption bond energies to transition metal surfaces and comparison to selected DFT functionals, Surf. Sci., 640, 36, 10.1016/j.susc.2015.03.023 · DOI · Paper page
  94. Christensen, 2015, Identifying systematic DFT errors in catalytic reactions, Catal. Sci. Technol., 5, 4946, 10.1039/C5CY01332A · DOI
  95. Landauer, 1957, Spatial variation of currents and fields due to localized scatterers in metallic conduction, IBM J. Res. Dev., 1, 223, 10.1147/rd.13.0223 · DOI
  96. Landauer, 1970, Electrical resistance of disordered one-dimensional lattices, Phil. Mag., 21, 863, 10.1080/14786437008238472 · DOI
  97. Caroli, 1971, Direct calculation of the tunneling current, J. Phys. C: Solid State Phys., 4, 916, 10.1088/0022-3719/4/8/018 · DOI
  98. Meir, 1992, Landauer formula for the current through an interacting electron region, Phys. Rev. Lett., 68, 2512, 10.1103/PhysRevLett.68.2512 · DOI
  99. Brandbyge, 2002, Density-functional method for nonequilibrium electron transport, Phys. Rev. B, 65, 10.1103/PhysRevB.65.165401 · DOI
  100. Strange, 2008, Benchmark density functional theory calculations for nanoscale conductance, J. Chem. Phys., 128, 10.1063/1.2839275 · DOI
  101. Guinea, 1983, Effective two-dimensional Hamiltonian at surfaces, Phys. Rev. B, 28, 4397, 10.1103/PhysRevB.28.4397 · DOI
  102. Larsen, 2009, Localized atomic basis set in the projector augmented wave method, Phys. Rev. B, 80, 10.1103/PhysRevB.80.195112 · DOI · Paper page
  103. Paulsson, 2007, Transmission eigenchannels from nonequilibrium Green’s functions, Phys. Rev. B, 76, 10.1103/PhysRevB.76.115117 · DOI
  104. Nakada, 1996, Edge state in graphene ribbons: nanometer size effect and edge shape dependence, Phys. Rev. B, 54, 17954, 10.1103/PhysRevB.54.17954 · DOI · Paper page
  105. Abell, 1985, Empirical chemical pseudopotential theory of molecular and metallic bonding, Phys. Rev. B, 31, 6184, 10.1103/PhysRevB.31.6184 · DOI
  106. Tersoff, 1986, New empirical model for the structural properties of silicon, Phys. Rev. Lett., 56, 632, 10.1103/PhysRevLett.56.632 · DOI
  107. Brenner, 1990, Empirical potential for hydrocarbons for use in simulating the chemical vapor deposition of diamond films, Phys. Rev. B, 42, 9458, 10.1103/PhysRevB.42.9458 · DOI
  108. Brenner, 2002, A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons, J. Phys.: Condens. Matter, 14, 783, 10.1088/0953-8984/14/4/312 · DOI
  109. Pastewka, 2012, Bond order potentials for fracture, wear and plasticity, MRS Bull., 37, 493, 10.1557/mrs.2012.94 · DOI
  110. Pastewka, 2013, Screened empirical bond-order potentials for Si–C, Phys. Rev. B, 87, 10.1103/PhysRevB.87.205410 · DOI
  111. Foiles, 2012, Contributions of the embedded-atom method to materials science and engineering, MRS Bull., 37, 485, 10.1557/mrs.2012.93 · DOI
  112. Elstner, 1998, Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties, Phys. Rev. B, 58, 7260, 10.1103/PhysRevB.58.7260 · DOI
  113. Bernstein, 2009, Hybrid atomistic simulation methods for materials systems, Rep. Prog. Phys., 72, 10.1088/0034-4885/72/2/026501 · DOI
  114. Skylaris, 2005, Introducing ONETEP: Linear-scaling density functional simulations on parallel computers, J. Chem. Phys., 122, 10.1063/1.1839852 · DOI · Paper page
  115. Salomon-Ferrer, 2012, An overview of the Amber biomolecular package, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 3, 198, 10.1002/wcms.1121 · DOI · Paper page

Cited by

See all 4,464

Metadata

Authors
Hjorth Larsen, A., Jørgen Mortensen, J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., Hermes, E. D., Jennings, P. C., Bjerre Jensen, P., Kermode, J., Kitchin, J. R., Leonhard Kolsbjerg, E., Kubal, J., Kaasbjerg, K., Lysgaard, S., Bergmann Maronsson, J., Maxson, T., Olsen, T., Pastewka, L., Peterson, A., Rostgaard, C., Schiøtz, J., Schütt, O., Strange, M., Thygesen, K. S., Vegge, T., Vilhelmsen, L., Walter, M., Zeng, Z., & Jacobsen, K. W.
Venue
Journal of Physics: Condensed Matter
Published
2017-06-07
Volume
29
Issue
27
Pages
273002
ISSN
  • 0953-8984 (ISSN-L)
  • 1361-648X
OpenAlex
W2601081289
Cited by
4,464
References
10
FWCI
144.65
Type
journal article
Language
en
Open access
bronze
Licence
Funders
  • Villum Fonden (grant no 9455)
  • Horizon 2020 Framework Programme (Grant no 676580)
  • Deutsche Forschungsgemeinschaft (Grant PA2023/2)
  • UK Engineering and Physical Sciences Research Council (EP/L014742/1)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.