Model name? MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002 Temperature (K)? No temperature given Cauchy stress (literal list of floats, Voigt order xx,yy,zz,yz,xz,xy, eV/A^3)? No stress given Runtime arguments (literal dictonary)? No runtime arguments given Initial parameters from query or test_generator (literal list of dicts)? [ { "property-id": "tag:staff@noreply.openkim.org,2023-02-21:property/crystal-structure-npt", "instance-id": 1, "prototype-label": { "source-value": "A_hP58_164_2d3i3j" }, "stoichiometric-species": { "source-value": [ "Si" ] }, "a": { "source-value": 9.914528679991331, "source-unit": "angstrom", "si-unit": "m", "si-value": 9.914528679991331e-10 }, "parameter-names": { "source-value": [ "c/a", "z1", "z2", "x3", "z3", "x4", "z4", "x5", "z5", "x6", "y6", "z6", "x7", "y7", "z7", "x8", "y8", "z8" ] }, "parameter-values": { "source-value": [ 1.6101550374088625, 0.4282817958702809, 0.5722471997866245, 0.13263638982253542, 0.5001622689497083, 0.2078071677174673, 0.6218966600216742, 0.20781377809827425, 0.37865003718268286, 0.6176878758792683, 0.6187192155213612, 0.29470202070232965, 0.9953882686340061, 0.24503199880864246, 0.17269176117616913, 0.3319740082038778, 0.42298325701829254, 0.05684354756135246 ] }, "cell-cauchy-stress": { "source-value": [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], "source-unit": "eV/angstrom^3", "si-unit": "kg / m s^2", "si-value": [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] }, "temperature": { "source-value": 0.0, "source-unit": "K", "si-unit": "K", "si-value": 0.0 }, "crystal-genome-source-structure-id": { "source-value": [ [ "RD_757650228553_000" ] ] }, "coordinates-file": { "source-value": "instance-1.poscar" }, "coordinates-file-conventional": { "source-value": "conventional.instance-1.poscar" }, "meta": { "uuid": "TE_300371409483_003-and-MO_557492625287_002-1752530586-tr", "path": "tr/TE_300371409483_003-and-MO_557492625287_002-1752530586-tr", "type": "tr", "_id": "TE_300371409483_003-and-MO_557492625287_002-1752530586-tr", "runner": { "extended-id": "EquilibriumCrystalStructure_A_hP58_164_2d3i3j_Si__TE_300371409483_003", "short-id": "TE_300371409483_003", "kimid-prefix": "EquilibriumCrystalStructure_A_hP58_164_2d3i3j_Si", "kimid-typecode": "te", "kimid-number": "300371409483", "kimid-version": "003", "kimid-version-as-integer": 3, "name": "EquilibriumCrystalStructure_A_hP58_164_2d3i3j_Si", "type": "te", "kimnum": "300371409483", "version": 3, "shortcode": "TE_300371409483", "kimcode": "EquilibriumCrystalStructure_A_hP58_164_2d3i3j_Si__TE_300371409483_003", "path": "te/EquilibriumCrystalStructure_A_hP58_164_2d3i3j_Si__TE_300371409483_003", "approved": true, "_id": "EquilibriumCrystalStructure_A_hP58_164_2d3i3j_Si__TE_300371409483_003", "makeable": true, "runner": true, "driver": { "extended-id": "EquilibriumCrystalStructure__TD_457028483760_003", "short-id": "TD_457028483760_003", "kimid-prefix": "EquilibriumCrystalStructure", "kimid-typecode": "td", "kimid-number": "457028483760", "kimid-version": "003", "kimid-version-as-integer": 3, "name": "EquilibriumCrystalStructure", "type": "td", "kimnum": "457028483760", "version": 3, "shortcode": "TD_457028483760", "kimcode": "EquilibriumCrystalStructure__TD_457028483760_003", "path": "td/EquilibriumCrystalStructure__TD_457028483760_003", "approved": true, "_id": "EquilibriumCrystalStructure__TD_457028483760_003", "makeable": true, "driver": true, "contributor-id": "4ad03136-ed7f-4316-b586-1e94ccceb311", "description": "Computes the equilibrium crystal structure and energy for an arbitrary crystal at zero temperature and applied stress by performing symmetry-constrained relaxation. The crystal structure is specified using the AFLOW prototype designation. Multiple sets of free parameters corresponding to the crystal prototype may be specified as initial guesses for structure optimization. No guarantee is made regarding the stability of computed equilibria, nor that any are the ground state.", "developer": [ "4ad03136-ed7f-4316-b586-1e94ccceb311", "360c0aed-48ce-45f6-ba13-337f12a531e8" ], "doi": "10.25950/866c7cfa", "domain": "openkim.org", "executables": [ "runner", "test_template/runner" ], "funding": [ { "award-number": "NSF DMR-1834251", "award-title": "Collaborative Research: Reliable Materials Simulation based on the Knowledgebase of Interatomic Models (KIM)", "funder-identifier": "https://doi.org/10.13039/100000001", "funder-identifier-type": "Crossref Funder ID", "funder-name": "National Science Foundation", "scheme-uri": "http://doi.org/" } ], "kim-api-version": "2.3", "maintainer-id": "4ad03136-ed7f-4316-b586-1e94ccceb311", "properties": [ "tag:staff@noreply.openkim.org,2023-02-21:property/binding-energy-crystal", "tag:staff@noreply.openkim.org,2023-02-21:property/crystal-structure-npt", "tag:staff@noreply.openkim.org,2025-04-15:property/mass-density-crystal-npt" ], "publication-year": "2025", "simulator-name": "ase", "source-citations": [ { "abstract": "Empirical databases of crystal structures and thermodynamic properties are fundamental tools for materials research. Recent rapid proliferation of computational data on materials properties presents the possibility to complement and extend the databases where the experimental data is lacking or difficult to obtain. Enhanced repositories that integrate both computational and empirical approaches open novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds, metastable structures and correlations between various characteristics. The practical realization of these opportunities depends on a systematic compilation and classification of the generated data in addition to an accessible interface for the materials science community. In this paper we present an extensive repository, aflowlib.org, comprising phase-diagrams, electronic structure and magnetic properties, generated by the high-throughput framework AFLOW. This continuously updated compilation currently contains over 150,000 thermodynamic entries for alloys, covering the entire composition range of more than 650 binary systems, 13,000 electronic structure analyses of inorganic compounds, and 50,000 entries for novel potential magnetic and spintronics systems. The repository is available for the scientific community on the website of the materials research consortium, aflowlib.org.", "author": "Curtarolo, Stefano and Setyawan, Wahyu and Wang, Shidong and Xue, Junkai and Yang, Kesong and Taylor, Richard H. and Nelson, Lance J. and Hart, Gus L.W. and Sanvito, Stefano and Buongiorno-Nardelli, Marco and Mingo, Natalio and Levy, Ohad", "doi": "https://doi.org/10.1016/j.commatsci.2012.02.002", "issn": "0927-0256", "journal": "Computational Materials Science", "keywords": "High-throughput, Combinatorial materials science, Ab initio, AFLOW, Materials genome initiative", "pages": "227-235", "recordkey": "TD_457028483760_003a", "recordtype": "article", "title": "{AFLOWLIB.ORG}: A distributed materials properties repository from high-throughput ab initio calculations", "url": "https://www.sciencedirect.com/science/article/pii/S0927025612000687", "volume": "58", "year": "2012" }, { "abstract": "To enable materials databases supporting computational and experimental research, it is critical to develop platforms that both facilitate access to the data and provide the tools used to generate/analyze it \u2014 all while considering the diversity of users\u2019 experience levels and usage needs. The recently formulated FAIR\u00a0principles (Findable, Accessible, Interoperable, and Reusable) establish a common framework to aid these efforts. This article describes aflow.org, a web ecosystem developed to provide FAIR-compliant access to the AFLOW\u00a0databases. Graphical and programmatic retrieval methods are offered, ensuring accessibility for all experience levels and data needs. aflow.org\u00a0goes beyond data-access by providing applications to important features of the AFLOW\u00a0software\u00a0[1], assisting users in their own calculations without the need to install the entire high-throughput framework. Outreach commitments to provide AFLOW\u00a0tutorials and materials science education to a global and diverse audiences will also be presented.", "author": "Esters, Marco and Oses, Corey and Divilov, Simon and Eckert, Hagen and Friedrich, Rico and Hicks, David and Mehl, Michael J. and Rose, Frisco and Smolyanyuk, Andriy and Calzolari, Arrigo and Campilongo, Xiomara and Toher, Cormac and Curtarolo, Stefano", "doi": "https://doi.org/10.1016/j.commatsci.2022.111808", "issn": "0927-0256", "journal": "Computational Materials Science", "keywords": "Autonomous materials science, Materials genome initiative, aflow, Computational ecosystems, Online tools, Database, Ab initio", "pages": "111808", "recordkey": "TD_457028483760_003b", "recordtype": "article", "title": "aflow.org: A web ecosystem of databases, software and tools", "url": "https://www.sciencedirect.com/science/article/pii/S0927025622005195", "volume": "216", "year": "2023" }, { "abstract": "The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets \u2014 facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.", "author": "Oses, Corey and Esters, Marco and Hicks, David and Divilov, Simon and Eckert, Hagen and Friedrich, Rico and Mehl, Michael J. and Smolyanyuk, Andriy and Campilongo, Xiomara and {van de Walle}, Axel and Schroers, Jan and Kusne, A. Gilad and Takeuchi, Ichiro and Zurek, Eva and Nardelli, Marco Buongiorno and Fornari, Marco and Lederer, Yoav and Levy, Ohad and Toher, Cormac and Curtarolo, Stefano", "doi": "https://doi.org/10.1016/j.commatsci.2022.111889", "issn": "0927-0256", "journal": "Computational Materials Science", "keywords": "AFLOW, Autonomous computation, Machine learning, Workflows", "pages": "111889", "recordkey": "TD_457028483760_003c", "recordtype": "article", "title": "aflow++: A {C}++ framework for autonomous materials design", "url": "https://www.sciencedirect.com/science/article/pii/S0927025622006000", "volume": "217", "year": "2023" } ], "title": "Equilibrium structure and energy for a crystal structure at zero temperature and pressure v003", "created_on": "2025-04-22 16:17:53.660578" }, "dependencies": [], "title": "Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP58_164_2d3i3j v003", "test-driver": "EquilibriumCrystalStructure__TD_457028483760_003", "species": [ "Si" ], "developer": [ "4ad03136-ed7f-4316-b586-1e94ccceb311", "360c0aed-48ce-45f6-ba13-337f12a531e8", "4d62befd-21c4-42b8-a472-86132e6591f3", "c4d2afd1-647e-4347-ae94-5e4772c16883" ], "description": "Computes the equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP58_164_2d3i3j at zero temperature and applied stress by performing symmetry-constrained relaxation. The following initial guess for the parameters (representing cell and internal degrees of freedom) allowed to vary during the relaxation is used:\na (angstrom): 10.017, c/a: 1.6093441, z1: 0.42717347, z2: 0.57149774, x3: 0.13353688, z3: 0.49951896, x4: 0.20865981, z4: 0.62176631, x5: 0.20852652, z5: 0.37695719, x6: 0.38057577, y6: 0.3795477, z6: 0.29513767, x7: 0.00061200299, y7: 0.24460663, z7: 0.17356382, x8: 0.33353138, y8: 0.4211924, z8: 0.058121322, obtained from OpenKIM Reference Data item RD_757650228553_000", "disclaimer": "Computer generated", "contributor-id": "4ad03136-ed7f-4316-b586-1e94ccceb311", "maintainer-id": "4ad03136-ed7f-4316-b586-1e94ccceb311", "kim-api-version": "2.3", "publication-year": "2025", "executables": [ "runner" ], "domain": "openkim.org", "matching-models": [ "standard-models" ], "created_on": "2025-07-14 21:42:50.949651" }, "subject": { "extended-id": "MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002", "short-id": "MO_557492625287_002", "kimid-prefix": "MEAM_LAMMPS_CuiGaoCui_2012_LiSi", "kimid-typecode": "mo", "kimid-number": "557492625287", "kimid-version": "002", "kimid-version-as-integer": 2, "name": "MEAM_LAMMPS_CuiGaoCui_2012_LiSi", "type": "mo", "kimnum": "557492625287", "version": 2, "shortcode": "MO_557492625287", "kimcode": "MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002", "path": "mo/MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002", "approved": true, "_id": "MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002", "makeable": true, "subject": true, "driver": { "extended-id": "MEAM_LAMMPS__MD_249792265679_002", "short-id": "MD_249792265679_002", "kimid-prefix": "MEAM_LAMMPS", "kimid-typecode": "md", "kimid-number": "249792265679", "kimid-version": "002", "kimid-version-as-integer": 2, "name": "MEAM_LAMMPS", "type": "md", "kimnum": "249792265679", "version": 2, "shortcode": "MD_249792265679", "kimcode": "MEAM_LAMMPS__MD_249792265679_002", "path": "md/MEAM_LAMMPS__MD_249792265679_002", "approved": true, "_id": "MEAM_LAMMPS__MD_249792265679_002", "makeable": true, "driver": true, "content-origin": "The model driver is implemented based on the MEAM (`meam`, `meam/spline`, and `meam/sw/spline`) package adapted from the LAMMPS software package and rewritten and updated by Yaser Afshar with performance improvements and extended to include support for an additional cutoff function.\n\nLAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator https://lammps.org", "contributor-id": "f9afb302-b4eb-4b55-a4e3-676ba64bfb77", "description": "The modified embedded atom method potential (MEAM)[1,2,3,4] model driver. The driver is written in C++ and implements three styles of modified embedded atom method (MEAM) potentials, `meam`, `meam/spline`, and `meam/sw/spline`. The style of the potential is automatically detected based on the input files to the driver. The input files are ASCII text files formatted to be consistent with the other MD codes that implement MEAM potentials, such as LAMMPS, serial DYNAMO code, and Warp. For any of the three styles mentioned above, the driver expects an element file. Depending on the specific potential style, other files may be required/supplied (a library and/or a parameter file for a `meam` style, and a potential file for a `meam/spline`, or `meam/sw/spline` style.)", "developer": [ "553f9aa4-98a2-477b-852f-a65cd9e1ace3", "05936d64-2312-402a-9873-5b6799e9f6db", "6ee0e203-4072-42b5-97a0-cf937edf5de8", "d5c826b2-1048-431c-bab6-0347f1c80c45", "98b95738-bd12-4464-9ed8-862e8be644e9", "f15f5ddf-8896-4f23-a4de-d96898caab64", "c8ad0beb-f4c8-4ddc-8a25-372f5cc4a17e", "57339548-c8c4-4b8b-a24b-6cecf2787096", "8ae4480b-2d4b-4f8c-b68d-6f8e2101d5a2", "d08eaec4-2289-4e6a-9fc7-c28d98c4156f", "cce68d90-29c8-48fa-a6fd-f806fa6d0f76", "a00983fc-9660-4769-82b0-5b90133a74be" ], "doi": "10.25950/ee5eba52", "domain": "openkim.org", "executables": [], "implementer": [ "f9afb302-b4eb-4b55-a4e3-676ba64bfb77", "a8c5e51f-f163-4842-b527-9ac69c3d33e2", "0f9bf091-9a1c-49e0-b107-a3bcc7d1dfa4", "27a42ac6-f00e-42a8-a1d3-54851ab2d08d", "d95e1403-9d6f-4dd4-ba80-1ccbf94dc75b", "44969c60-361d-4f11-87b8-6a5e35597d34", "741dc3be-59fb-4e5b-8653-c63be9d4ee5d", "e632a391-ea42-4bf6-8737-e71c296a067a" ], "kim-api-version": "2.2", "maintainer-id": "f9afb302-b4eb-4b55-a4e3-676ba64bfb77", "publication-year": "2023", "simulator-potential-compatibility": [ { "compatibility": "full", "simulator-name": "LAMMPS", "simulator-potential": "meam" }, { "compatibility": "full", "simulator-name": "LAMMPS", "simulator-potential": "meam/spline" }, { "compatibility": "full", "simulator-name": "LAMMPS", "simulator-potential": "meam/sw/spline" } ], "source-citations": [ { "author": "Baskes, M.I. and Nelson, J.S. and Wright, A.F.", "doi": "10.1103/PhysRevB.40.6085", "journal": "Phys. Rev. B", "pages": "6085--6100", "recordkey": "MD_249792265679_002a", "recordtype": "article", "title": "Semiempirical modified embedded-atom potentials for silicon and germanium", "volume": "40", "year": "1989" }, { "author": "Baskes, M.I.", "doi": "10.1103/PhysRevB.46.2727", "journal": "Phys. Rev. B", "pages": "2727--2742", "recordkey": "MD_249792265679_002b", "recordprimary": "recordprimary", "recordtype": "article", "title": "Modified embedded-atom potentials for cubic materials and impurities", "volume": "46", "year": "1992" }, { "author": "Lee, B.J. and Baskes, M.I.", "doi": "10.1103/PhysRevB.62.8564", "journal": "Phys. Rev. B", "pages": "8564--8567", "recordkey": "MD_249792265679_002c", "recordtype": "article", "title": "Second nearest-neighbor modified embedded-atom-method potential", "volume": "62", "year": "2000" }, { "author": "Lenosky, T.J. and Sadigh, B. and Alonso, E. and Bulatov, V.V. and de la Rubia, T.D. and Kim, J. and Voter, A.F. and Kress, J.D.", "doi": "10.1088/0965-0393/8/6/305", "journal": "Model. Simul. Mat. Sci. Eng", "pages": "825--841", "recordkey": "MD_249792265679_002d", "recordtype": "article", "title": "Highly optimized empirical potential model of silicon", "volume": "8", "year": "2000" } ], "title": "The modified embedded atom method (MEAM) potential v002", "created_on": "2024-10-01 20:38:58.516366" }, "content-origin": "https://doi.org/10.1016/j.jpowsour.2012.01.145\\nhttps://doi.org/10.1088/0965-0393/20/1/015014", "content-other-locations": "https://openkim.org/id/MEAM_2NN_LiSi__MO_596436139350_001 (retired)\\nhttps://openkim.org/id/Sim_LAMMPS_MEAM_CuiGaoCui_2012_LiSi__SM_562938628131_000", "contributor-id": "f9afb302-b4eb-4b55-a4e3-676ba64bfb77", "description": "A second nearest-neighbor modified embedded atom method (2NN MEAM) interatomic potential for lithium-silicon (Li-Si) alloys developed by using the particle swarm optimization (PSO) method in conjunction with ab initio calculations. This interatomic potential is capable of simulating the transition from disordered to ordered states of Li-Si crystalline structures, an indication of the stability and robustness of the interatomic potential at finite temperature. In the paper (Cui et al., J. Power Sources, 207:150-159, 2012), examples are given demonstrating that the new interatomic potential is also capable of predicting the material properties of both crystalline and amorphous Li-Si alloys, including the elastic modulus, compositional expansion, the diffusivity of Li in Li-Si alloys, and plastic yield strength.", "developer": [ "86692981-fddb-4a42-b693-3ce653215cc8", "4011dd67-e162-4834-b1d6-8923d6133ef4", "05928fd1-3006-44d7-914b-a7d1a0738d57", "b343ba44-0603-4923-920b-c1598e15745b" ], "doi": "10.25950/2d2bdf75", "domain": "openkim.org", "executables": [], "kim-api-version": "2.2", "maintainer-id": "f9afb302-b4eb-4b55-a4e3-676ba64bfb77", "model-driver": "MEAM_LAMMPS__MD_249792265679_002", "potential-type": "meam", "publication-year": "2023", "source-citations": [ { "author": "Cui, Z. and Gao, F. and Cui, Z. and Qu, J.", "doi": "10.1016/j.jpowsour.2012.01.145", "journal": "Journal of Power Sources", "month": "", "note": "", "number": "", "pages": "150--159", "recordkey": "MO_557492625287_002a", "recordprimary": "recordprimary", "recordtype": "article", "title": "A second nearest-neighbor embedded atom method interatomic potential for {Li}-{Si} alloys", "volume": "207", "year": "2012" } ], "species": [ "Li", "Si" ], "title": "MEAM potential for Li-Si alloys developed by Cui et al. (2012) v002", "created_on": "2023-05-09 18:54:16.351844" }, "test": "EquilibriumCrystalStructure_A_hP58_164_2d3i3j_Si__TE_300371409483_003", "model": "MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002", "domain": "openkim.org", "test-result-id": "TE_300371409483_003-and-MO_557492625287_002-1752530586-tr", "created_on": "2025-07-14 22:59:06.153917", "dependencies": [] }, "created_on": "2025-07-14 22:59:06.153917", "inserted_on": "2025-07-15 01:02:01.670536", "latest": true } ] NOTE: The configuration you provided has a maximum force component 0.0017369294911145916 eV/angstrom. Unless the Test Driver you are running provides minimization, you may wish to relax the configuration. E L A S T I C C O N S T A N T C A L C U L A T I O N S Summary of completed elastic constants calculation: Method: energy-condensed Step generator: MaxStepGenerator(_base_step=0.0001,_step_nom=None,_num_steps=14,_step_ratio=1.6,offset=0,num_extrap=9,check_num_steps=True,use_exact_steps=True,_scale=500,_state=State(x=array([0., 0., 0., 0., 0., 0.]), method='central', n=2, order=2)) Raw elastic constants [ASE units]: [[ -244.48633 313.93265 0.2636 -2665.35815 -0.09608 0.01949] [ 313.93265 -488.96478 0.25949 3106.51787 0.32593 -0.11724] [ 0.2636 0.25949 0.89748 -0.27432 0.03669 -0.02525] [-2665.35815 3106.51787 -0.27432 6952.04045 0.22213 -0.03938] [ -0.09608 0.32593 0.03669 0.22213 6961.90789 -14.2175 ] [ 0.01949 -0.11724 -0.02525 -0.03938 -14.2175 -1763.59202]] 95%% Error estimate [ASE units]: [[ 1504.54638 7687.38921 0.14521 19891.35984 0.17778 0.14912] [ 7687.38921 5161.23086 0.15533 23330.53894 1.38031 2.67044] [ 0.14521 0.15533 0.00001 2.8055 0.78675 1.12085] [ 19891.35984 23330.53894 2.8055 300475.24108 0.55491 0.08399] [ 0.17778 1.38031 0.78675 0.55491 121641.04837 436.69967] [ 0.14912 2.67044 1.12085 0.08399 436.69967 9322.96284]] Relative norm of error estimate: 30.147558081373074 Relative norm of deviation from material symmetry: 0.2831662521151607 Summary of completed elastic constants calculation: Method: energy-condensed Step generator: MaxStepGenerator(_base_step=0.001,_step_nom=None,_num_steps=14,_step_ratio=1.6,offset=0,num_extrap=9,check_num_steps=True,use_exact_steps=True,_scale=500,_state=State(x=array([0., 0., 0., 0., 0., 0.]), method='central', n=2, order=2)) Raw elastic constants [ASE units]: [[ 597.90867 45.33559 -1905.87678 -16769.57658 0. 0.00001] [ 45.33559 -51.89023 20934.24358 834.86371 -0. 0.0019 ] [ -1905.87678 20934.24358 0.89746 0.4778 0. -0.00009] [-16769.57658 834.86371 0.4778 -1475.26016 -0. -0. ] [ 0. -0. 0. -0. -1905.19167 9.7906 ] [ 0.00001 0.0019 -0.00009 -0. 9.7906 -44.21946]] 95%% Error estimate [ASE units]: [[ 3025.61084 249.9402 23750.31048 10995.03827 0.00034 0.01325] [ 249.9402 496.45981 23523.19087 14837.35681 0.00907 0.0071 ] [23750.31048 23523.19087 0. 171.12916 0.00004 0.01267] [10995.03827 14837.35681 171.12916 948.94741 0.00007 0.00013] [ 0.00034 0.00907 0.00004 0.00007 11617.60025 48.98139] [ 0.01325 0.0071 0.01267 0.00013 48.98139 23.75569]] Relative norm of error estimate: 1.428407450392121 Relative norm of deviation from material symmetry: 0.8116872009290496 Summary of completed elastic constants calculation: Method: stress-condensed Step generator: MaxStepGenerator(_base_step=0.0001,_step_nom=None,_num_steps=14,_step_ratio=1.6,offset=0,num_extrap=9,check_num_steps=True,use_exact_steps=True,_scale=500,_state=State(x=array([0., 0., 0., 0., 0., 0.]), method='central', n=1, order=2)) Raw elastic constants [ASE units]: [[ 0.86266 0.35675 0.25976 0.87087 0. 0.00006] [ 0.35675 0.86266 0.25975 -0.87364 -0. 0.00005] [ 0.25976 0.25975 0.89885 -0.00338 -0.00135 0.00084] [ 0.87087 -0.87364 -0.00338 -30.67766 0. -0. ] [ 0. -0. -0.00135 0. -30.69119 0.99106] [ 0.00006 0.00005 0.00084 -0. 0.99106 -0.10877]] 95%% Error estimate [ASE units]: [[ 2.12406 1.48192 0.018 0.40026 0.00065 0.00057] [ 1.48192 2.12405 0.01778 0.39214 0.00033 0.0005 ] [ 0.018 0.01778 0.00015 0.00655 0.00351 0.00133] [ 0.40026 0.39214 0.00655 16.76395 0. 0. ] [ 0.00065 0.00033 0.00351 0. 16.77722 2.37829] [ 0.00057 0.0005 0.00133 0. 2.37829 17.34233]] Relative norm of error estimate: 0.6821658157723197 Relative norm of deviation from material symmetry: 0.006496246311799936 Summary of completed elastic constants calculation: Method: stress-condensed Step generator: MaxStepGenerator(_base_step=0.001,_step_nom=None,_num_steps=14,_step_ratio=1.6,offset=0,num_extrap=9,check_num_steps=True,use_exact_steps=True,_scale=500,_state=State(x=array([0., 0., 0., 0., 0., 0.]), method='central', n=1, order=2)) Raw elastic constants [ASE units]: [[ -0.01811 0.96567 22.81784 3.25208 -0. -0.00001] [ 0.96567 -41.7201 -0.85864 -11.42316 -0. 0.00006] [ 22.81784 -0.85864 0.89745 -0.07941 0. -0.00048] [ 3.25208 -11.42316 -0.07941 9.20413 -0. 0.00001] [ -0. -0. 0. -0. 0.35887 2.14649] [ -0.00001 0.00006 -0.00048 0.00001 2.14649 -4.86069]] 95%% Error estimate [ASE units]: [[91.70633 23.08687 12.57151 3.15431 0. 0.00001] [23.08687 36.48035 34.71351 26.76797 0. 0.00002] [12.57151 34.71351 0.00016 3.72017 0. 0.0003 ] [ 3.15431 26.76797 3.72017 11.59445 0. 0.00002] [ 0. 0. 0. 0. 15.23251 18.51908] [ 0.00001 0.00002 0.0003 0.00002 18.51908 7.58632]] Relative norm of error estimate: 2.340639722214392 Relative norm of deviation from material symmetry: 0.7053973353651275 Summary of completed elastic constants calculation: Method: stress-condensed Step generator: MaxStepGenerator(_base_step=0.01,_step_nom=None,_num_steps=14,_step_ratio=1.6,offset=0,num_extrap=9,check_num_steps=True,use_exact_steps=True,_scale=500,_state=State(x=array([0., 0., 0., 0., 0., 0.]), method='central', n=1, order=2)) Raw elastic constants [ASE units]: [[-2.47115 4.10998 1.24109 1.57608 -0. 0. ] [ 4.10998 -7.29179 -0.69922 1.9991 0. -0. ] [ 1.24109 -0.69922 0.89746 -0.00553 0. 0. ] [ 1.57608 1.9991 -0.00553 0.43345 -0. -0. ] [-0. 0. 0. -0. 0.36496 0.25064] [ 0. -0. 0. -0. 0.25064 0.06302]] 95%% Error estimate [ASE units]: [[0.50817 2.23283 4.07546 1.73566 0. 0. ] [2.23283 3.66396 7.10635 4.91256 0. 0. ] [4.07546 7.10635 0.00001 0.16466 0. 0. ] [1.73566 4.91256 0.16466 0.57908 0. 0. ] [0. 0. 0. 0. 0.68888 1.87019] [0. 0. 0. 0. 1.87019 3.63995]] Relative norm of error estimate: 1.6708549511191257 Relative norm of deviation from material symmetry: 0.8145936526790896 Summary of completed elastic constants calculation: Method: stress-condensed Step generator: MaxStepGenerator(_base_step=0.1,_step_nom=None,_num_steps=14,_step_ratio=1.6,offset=0,num_extrap=9,check_num_steps=True,use_exact_steps=True,_scale=500,_state=State(x=array([0., 0., 0., 0., 0., 0.]), method='central', n=1, order=2)) Raw elastic constants [ASE units]: [[ 0.70208 0.53544 0.31374 0.23181 -0. 0. ] [ 0.53544 0.63802 0.20862 -0.28218 -0. 0. ] [ 0.31374 0.20862 0.89744 0.02068 -0. 0. ] [ 0.23181 -0.28218 0.02068 0.2395 0. -0. ] [-0. -0. -0. 0. 0.17203 0.26912] [ 0. 0. 0. -0. 0.26912 -0.37721]] 95%% Error estimate [ASE units]: [[0.56073 0.47284 0.03535 0.11485 0. 0. ] [0.47284 0.67941 0.02565 0.1765 0. 0. ] [0.03535 0.02565 0.00004 0.02498 0. 0. ] [0.11485 0.1765 0.02498 0.38681 0. 0. ] [0. 0. 0. 0. 0.28919 0.62705] [0. 0. 0. 0. 0.62705 0.35531]] Relative norm of error estimate: 1.1418900427063108 Relative norm of deviation from material symmetry: 0.30406794089178696 Elastic constants calculation had a relative 95% uncertainty greater than 0.02 and/or relative deviation from material symmetry greater than 0.01. See stdout and logs for calculation details. The following run was chosen as having the lowest error: Method: stress-condensed Step generator: MaxStepGenerator(_base_step=0.0001,_step_nom=None,_num_steps=14,_step_ratio=1.6,offset=0,num_extrap=9,check_num_steps=True,use_exact_steps=True,_scale=500,_state=State(x=array([0., 0., 0., 0., 0., 0.]), method='central', n=1, order=2)) Raw elastic constants [ASE units]: [[ 0.86266 0.35675 0.25976 0.87087 0. 0.00006] [ 0.35675 0.86266 0.25975 -0.87364 -0. 0.00005] [ 0.25976 0.25975 0.89885 -0.00338 -0.00135 0.00084] [ 0.87087 -0.87364 -0.00338 -30.67766 0. -0. ] [ 0. -0. -0.00135 0. -30.69119 0.99106] [ 0.00006 0.00005 0.00084 -0. 0.99106 -0.10877]] 95%% Error estimate [ASE units]: [[ 2.12406 1.48192 0.018 0.40026 0.00065 0.00057] [ 1.48192 2.12405 0.01778 0.39214 0.00033 0.0005 ] [ 0.018 0.01778 0.00015 0.00655 0.00351 0.00133] [ 0.40026 0.39214 0.00655 16.76395 0. 0. ] [ 0.00065 0.00033 0.00351 0. 16.77722 2.37829] [ 0.00057 0.0005 0.00133 0. 2.37829 17.34233]] Relative norm of error estimate: 0.6821658157723197 Relative norm of deviation from material symmetry: 0.006496246311799936 R E S U L T S Elastic constants [GPa]: [[ 138.21369 57.15834 41.61842 139.52866 0.00025 0.00981] [ 57.15834 138.21396 41.61664 -139.97246 -0.00019 0.00832] [ 41.61842 41.61664 144.01212 -0.54185 -0.21551 0.13432] [ 139.52866 -139.97246 -0.54185 -4915.10362 0.00014 -0.00002] [ 0.00025 -0.00019 -0.21551 0.00014 -4917.27123 158.78566] [ 0.00981 0.00832 0.13432 -0.00002 158.78566 -17.42614]] 95 %% Error estimate [GPa]: [[ 340.31221 237.42947 2.8833 64.12841 0.10469 0.09054] [ 237.42947 340.31016 2.84789 62.82807 0.053 0.08053] [ 2.8833 2.84789 0.02437 1.04986 0.56234 0.21273] [ 64.12841 62.82807 1.04986 2685.88074 0.0002 0.00046] [ 0.10469 0.053 0.56234 0.0002 2688.00641 381.04464] [ 0.09054 0.08053 0.21273 0.00046 381.04464 2778.54764]] Bulk modulus [GPa] = 77.84774298676983 Unique elastic constants for space group 164 [GPa] ['c11', 'c12', 'c13', 'c14', 'c33', 'c44'] [109.23688323072783, 86.13528329910147, 41.61752902466437, 149.26810835442063, 144.0121232652984, -4916.187427357598] WARNING: Nearest isotropic state not computed.