Model name? MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001 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, "disclaimer": "The forces and stresses failed to converge to the requested tolerance", "prototype-label": { "source-value": "A3B5_oC16_65_ah_bej" }, "stoichiometric-species": { "source-value": [ "Al", "Ni" ] }, "a": { "source-value": 7.544951343214067, "source-unit": "angstrom", "si-unit": "m", "si-value": 7.544951343214067e-10 }, "parameter-names": { "source-value": [ "b/a", "c/a", "x4", "y5" ] }, "parameter-values": { "source-value": [ 1.087778901471642, 0.4950359513022323, 0.765159672114496, 0.7334618594374349 ] }, "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_630670196626_000" ] ] }, "coordinates-file": { "source-value": "instance-1.poscar" }, "coordinates-file-conventional": { "source-value": "conventional.instance-1.poscar" }, "meta": { "uuid": "TE_916164216045_003-and-MO_461927113651_001-1753136860-tr", "path": "tr/TE_916164216045_003-and-MO_461927113651_001-1753136860-tr", "type": "tr", "_id": "TE_916164216045_003-and-MO_461927113651_001-1753136860-tr", "runner": { "extended-id": "EquilibriumCrystalStructure_A3B5_oC16_65_ah_bej_AlNi__TE_916164216045_003", "short-id": "TE_916164216045_003", "kimid-prefix": "EquilibriumCrystalStructure_A3B5_oC16_65_ah_bej_AlNi", "kimid-typecode": "te", "kimid-number": "916164216045", "kimid-version": "003", "kimid-version-as-integer": 3, "name": "EquilibriumCrystalStructure_A3B5_oC16_65_ah_bej_AlNi", "type": "te", "kimnum": "916164216045", "version": 3, "shortcode": "TE_916164216045", "kimcode": "EquilibriumCrystalStructure_A3B5_oC16_65_ah_bej_AlNi__TE_916164216045_003", "path": "te/EquilibriumCrystalStructure_A3B5_oC16_65_ah_bej_AlNi__TE_916164216045_003", "approved": true, "_id": "EquilibriumCrystalStructure_A3B5_oC16_65_ah_bej_AlNi__TE_916164216045_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 AlNi in AFLOW crystal prototype A3B5_oC16_65_ah_bej v003", "test-driver": "EquilibriumCrystalStructure__TD_457028483760_003", "species": [ "Al", "Ni" ], "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 AlNi in AFLOW crystal prototype A3B5_oC16_65_ah_bej 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): 7.4432, b/a: 0.87731083, c/a: 0.50173313, x4: 0.71933153, y5: 0.77797272, obtained from OpenKIM Reference Data item RD_630670196626_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": "2025-07-21 22:27:02.278568" }, "subject": { "extended-id": "MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001", "short-id": "MO_461927113651_001", "kimid-prefix": "MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi", "kimid-typecode": "mo", "kimid-number": "461927113651", "kimid-version": "001", "kimid-version-as-integer": 1, "name": "MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi", "type": "mo", "kimnum": "461927113651", "version": 1, "shortcode": "MO_461927113651", "kimcode": "MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001", "path": "mo/MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001", "approved": true, "_id": "MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001", "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://www.ctcms.nist.gov/potentials/entry/2022--Mahata-A-Mukhopadhyay-T-Asle-Zaeem-M--Al-Ni/", "contributor-id": "4ad03136-ed7f-4316-b586-1e94ccceb311", "description": "This is a second nearest neighbor modified embedded-atom method (2NN-MEAM) interatomic potential for Al-Ni.", "developer": [ "1890a63e-f66e-4393-aab9-a9fac6eac6bc", "b0c58d1f-5985-4323-b4bb-4b5c53d2a922", "53e43133-8a1e-4eba-b421-baa3cfca1621" ], "doi": "10.25950/5d4f85ba", "domain": "openkim.org", "executables": [], "kim-api-version": "2.2", "maintainer-id": "4ad03136-ed7f-4316-b586-1e94ccceb311", "model-driver": "MEAM_LAMMPS__MD_249792265679_002", "potential-type": "meam", "publication-year": "2023", "source-citations": [ { "abstract": "Second nearest neighbor modified embedded-atom method (2NN-MEAM) interatomic potentials are developed for binary aluminum (Al) alloys applicable from room temperature to the melting point. The binary alloys studied in this work are Al-Cu, Al-Fe and Al-Ni. Sensitivity and uncertainty analyses are performed on potential parameters based on the perturbation approach. The outcome of the sensitivity analysis shows that some of the MEAM parameters interdependently influence all MEAM model outputs, allowing for the definition of an ordered calibration procedure to target specific MEAM outputs. Using these 2NN-MEAM interatomic potentials, molecular dynamics (MD) simulations are performed to calculate low and high-temperature properties, such as the formation energies of stable phases and unstable intermetallics, lattice parameters, elastic constants, thermal expansion coefficients, enthalpy of formation of solids, liquid mixing enthalpy, and liquidus temperatures at a wide range of compositions. The computed data are compared with the available first principle calculations and experimental data, showing high accuracy of the 2NN-MEAM interatomic potentials. In addition, the liquidus temperature of the Al binary alloys is compared to the phase diagrams determined by the CALPHAD method.", "author": "Mahata, Avik and Mukhopadhyay, Tanmoy and {Asle Zaeem}, Mohsen", "doi": "https://doi.org/10.1016/j.commatsci.2021.110902", "issn": "0927-0256", "journal": "Computational Materials Science", "keywords": "Interatomic potentials, Binary aluminum alloys, Melting, Molecular dynamics", "pages": "110902", "recordkey": "MO_461927113651_001a", "recordprimary": "recordprimary", "recordtype": "article", "title": "Modified embedded-atom method interatomic potentials for {Al-Cu, Al-Fe and Al-Ni} binary alloys: From room temperature to melting point", "url": "https://www.sciencedirect.com/science/article/pii/S0927025621006108", "volume": "201", "year": "2022" } ], "species": [ "Al", "Ni" ], "title": "MEAM Potential for the Al-Ni system developed by Mahata, Mukhopadhyay and Asle Zaeem (2022) v001", "created_on": "2023-05-09 19:10:23.055982" }, "test": "EquilibriumCrystalStructure_A3B5_oC16_65_ah_bej_AlNi__TE_916164216045_003", "model": "MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001", "domain": "openkim.org", "disclaimer": "instance-id 1: The forces and stresses failed to converge to the requested tolerance\ninstance-id 2: The forces and stresses failed to converge to the requested tolerance\ninstance-id 3: The forces and stresses failed to converge to the requested tolerance\n", "test-result-id": "TE_916164216045_003-and-MO_461927113651_001-1753136860-tr", "created_on": "2025-07-22 22:30:02.661754", "dependencies": [] }, "created_on": "2025-07-22 22:30:02.661754", "inserted_on": "2025-07-22 22:40:33.022308", "latest": true } ] NOTE: The configuration you provided has a maximum force component 0.0034492218102335492 eV/angstrom. Unless the Test Driver you are running provides minimization, you may wish to relax the configuration. NOTE: The configuration you provided has a maximum stress component 0.00019170204332325173 eV/angstrom^3 even though the nominal state of the system is unstressed. 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]: [[ 0.33324 0.65683 0.6532 0. 0. 0. ] [ 0.65683 1.57465 0.7623 -0. -0. -0. ] [ 0.6532 0.7623 1.10573 0. -0. -0. ] [ 0. -0. 0. 0.33421 0. 0. ] [ 0. -0. -0. 0. 0.45786 -0. ] [ 0. -0. -0. 0. -0. 0.42311]] 95%% Error estimate [ASE units]: [[0. 0. 0. 0. 0. 0. ] [0. 0. 0. 0. 0. 0. ] [0. 0. 0. 0. 0. 0. ] [0. 0. 0. 0.00001 0. 0. ] [0. 0. 0. 0. 0.00004 0. ] [0. 0. 0. 0. 0. 0.00015]] Relative norm of error estimate: 0.00010777933498392733 Relative norm of deviation from material symmetry: 3.0459927869185846e-07 R E S U L T S Elastic constants [GPa]: [[ 53.39021 105.23653 104.65424 0. 0.00002 0.00001] [105.23653 252.28726 122.13468 -0.00003 -0.00006 -0.00001] [104.65424 122.13468 177.15689 0.00002 -0. -0. ] [ 0. -0.00003 0.00002 53.54653 0. 0. ] [ 0.00002 -0.00006 -0. 0. 73.35669 -0. ] [ 0.00001 -0.00001 -0. 0. -0. 67.78912]] 95 %% Error estimate [GPa]: [[0.00031 0.00001 0.00011 0.00022 0.00063 0.00024] [0.00001 0.00011 0.00028 0.00015 0.00012 0.00014] [0.00011 0.00028 0.00005 0.0001 0.00043 0.00004] [0.00022 0.00015 0.0001 0.00143 0. 0.00022] [0.00063 0.00012 0.00043 0. 0.00613 0.0002 ] [0.00024 0.00014 0.00004 0.00022 0.0002 0.02465]] Bulk modulus [GPa] = -529.2118635895512 Unique elastic constants for space group 65 [GPa] ['c11', 'c12', 'c13', 'c22', 'c23', 'c33', 'c44', 'c55', 'c66'] [53.3902110657288, 105.23652597887106, 104.65424153011485, 252.28725562438035, 122.1346811967319, 177.1568863897718, 53.54652535369143, 73.35668799510405, 67.78911809782976] WARNING: Nearest isotropic state not computed.