Model name? ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000 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": "A_tP106_137_a5g4h" }, "stoichiometric-species": { "source-value": [ "Si" ] }, "a": { "source-value": 10.208583078135183, "source-unit": "angstrom", "si-unit": "m", "si-value": 1.020858307813518e-09 }, "parameter-names": { "source-value": [ "c/a", "y2", "z2", "y3", "z3", "y4", "z4", "y5", "z5", "y6", "z6", "x7", "y7", "z7", "x8", "y8", "z8", "x9", "y9", "z9", "x10", "y10", "z10" ] }, "parameter-values": { "source-value": [ 2.3149093673364147, 0.36805354099205, 0.7745430414792343, 0.8967187351622834, 0.09862441930588117, 0.9420508440469157, 0.19599742744067394, 0.9759119089300992, 0.8508271515475987, 0.13463884966533635, 0.9224171530979035, 0.4249000403519887, 0.4709982305430469, 0.06493686156496392, 0.5446438439010506, 0.13261745348248977, 0.13920621619122642, 0.566377075795117, 0.9335612470354755, 0.2783391602808116, 0.8651818694729891, 0.9420943055154336, 0.5241453078021376 ] }, "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_916506373351_000" ] ] }, "coordinates-file": { "source-value": "instance-1.poscar" }, "coordinates-file-conventional": { "source-value": "conventional.instance-1.poscar" }, "meta": { "uuid": "TE_330658189378_003-and-MO_566683736730_000-1752531718-tr", "path": "tr/TE_330658189378_003-and-MO_566683736730_000-1752531718-tr", "type": "tr", "_id": "TE_330658189378_003-and-MO_566683736730_000-1752531718-tr", "runner": { "extended-id": "EquilibriumCrystalStructure_A_tP106_137_a5g4h_Si__TE_330658189378_003", "short-id": "TE_330658189378_003", "kimid-prefix": "EquilibriumCrystalStructure_A_tP106_137_a5g4h_Si", "kimid-typecode": "te", "kimid-number": "330658189378", "kimid-version": "003", "kimid-version-as-integer": 3, "name": "EquilibriumCrystalStructure_A_tP106_137_a5g4h_Si", "type": "te", "kimnum": "330658189378", "version": 3, "shortcode": "TE_330658189378", "kimcode": "EquilibriumCrystalStructure_A_tP106_137_a5g4h_Si__TE_330658189378_003", "path": "te/EquilibriumCrystalStructure_A_tP106_137_a5g4h_Si__TE_330658189378_003", "approved": true, "_id": "EquilibriumCrystalStructure_A_tP106_137_a5g4h_Si__TE_330658189378_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_tP106_137_a5g4h 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_tP106_137_a5g4h 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): 10.1614, c/a: 2.3850257, y2: 0.93992864, z2: 0.19609985, y3: 0.87847441, z3: 0.10226691, y4: 0.13030281, z4: 0.77487209, y5: 0.98453855, z5: 0.85341975, y6: 0.13332475, z6: 0.92680675, x7: 0.43292986, y7: 0.065760748, z7: 0.72131569, x8: 0.45643579, y8: 0.87171028, z8: 0.86152967, x9: 0.57092716, y9: 0.9958931, z9: 0.93303081, x10: 0.43052092, y10: 0.13256247, z10: 0.98508065, obtained from OpenKIM Reference Data item RD_916506373351_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 21:43:16.245056" }, "subject": { "extended-id": "ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000", "short-id": "MO_566683736730_000", "kimid-prefix": "ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si", "kimid-typecode": "mo", "kimid-number": "566683736730", "kimid-version": "000", "kimid-version-as-integer": 0, "name": "ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si", "type": "mo", "kimnum": "566683736730", "version": 0, "shortcode": "MO_566683736730", "kimcode": "ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000", "path": "mo/ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000", "approved": true, "_id": "ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000", "makeable": true, "subject": true, "driver": { "extended-id": "ThreeBodyBondOrder_PPM__MD_184422512875_000", "short-id": "MD_184422512875_000", "kimid-prefix": "ThreeBodyBondOrder_PPM", "kimid-typecode": "md", "kimid-number": "184422512875", "kimid-version": "000", "kimid-version-as-integer": 0, "name": "ThreeBodyBondOrder_PPM", "type": "md", "kimnum": "184422512875", "version": 0, "shortcode": "MD_184422512875", "kimcode": "ThreeBodyBondOrder_PPM__MD_184422512875_000", "path": "md/ThreeBodyBondOrder_PPM__MD_184422512875_000", "approved": true, "_id": "ThreeBodyBondOrder_PPM__MD_184422512875_000", "makeable": true, "driver": true, "contributor-id": "c4d2afd1-647e-4347-ae94-5e4772c16883", "description": "This is a model driver for an optimized interatomic potential based on a modified Tersoff form originally constructed for bulk silicon and 2D silicon (silicene). The silicon potential reproduces a wide range of properties of Si and improves over existing potentials with respect to point defect structures and energies, surface energies and reconstructions, thermal expansion, melting temperature and other properties.", "developer": [ "02eb69dc-bae3-460c-aaf4-1513724c986f", "201572cf-870c-477f-b34f-3a6481002dee" ], "doi": "10.25950/23639742", "domain": "openkim.org", "executables": [ "bondorder.inc", "bondorder_aux.inc" ], "implementer": [ "c4d2afd1-647e-4347-ae94-5e4772c16883", "4d62befd-21c4-42b8-a472-86132e6591f3" ], "kim-api-version": "2.0", "maintainer-id": "c4d2afd1-647e-4347-ae94-5e4772c16883", "publication-year": "2019", "source-citations": [ { "author": "Pun, G. P. Purja and Mishin, Y.", "doi": "10.1103/PhysRevB.95.224103", "issue": "22", "journal": "Phys. Rev. B", "numpages": "21", "pages": "224103", "recordkey": "MD_184422512875_000a", "recordtype": "article", "title": "Optimized interatomic potential for silicon and its application to thermal stability of silicene", "volume": "95", "year": "2017" } ], "title": "Three-body bond-order (Tersoff-style) potential by Purja Pun and Mishin (2017) v000", "created_on": "2023-11-10 21:08:50.372998" }, "contributor-id": "c4d2afd1-647e-4347-ae94-5e4772c16883", "description": "An optimized interatomic potential for silicon using a modified Tersoff model. The potential reproduces a wide range of properties of Si and improves over existing potentials with respect to point defect structures and energies, surface energies and reconstructions, thermal expansion, melting temperature and other properties. The proposed potential is compared with three other potentials from the literature. The potentials demonstrate reasonable agreement with first-principles binding energies of small Si clusters as well as single-layer and bilayer silicenes. The four potentials are used to evaluate the thermal stability of free-standing silicenes in the form of nano-ribbons, nano-flakes, and nano-tubes. While single-layer silicene is mechanically stable at zero Kelvin, it is predicted to become unstable and collapse at room temperature. By contrast, the bilayer silicene demonstrates a larger bending rigidity and remains stable at and even above room temperature. The results suggest that bilayer silicene might exist in a free-standing form at ambient conditions.", "developer": [ "02eb69dc-bae3-460c-aaf4-1513724c986f", "201572cf-870c-477f-b34f-3a6481002dee" ], "doi": "10.25950/7495bb93", "domain": "openkim.org", "kim-api-version": "2.0", "maintainer-id": "c4d2afd1-647e-4347-ae94-5e4772c16883", "model-driver": "ThreeBodyBondOrder_PPM__MD_184422512875_000", "potential-type": "ppm", "publication-year": "2019", "source-citations": [ { "author": "Pun, G. P. Purja and Mishin, Y.", "doi": "10.1103/PhysRevB.95.224103", "issue": "22", "journal": "Phys. Rev. B", "numpages": "21", "pages": "224103", "recordkey": "MO_566683736730_000a", "recordprimary": "recordprimary", "recordtype": "article", "title": "Optimized interatomic potential for silicon and its application to thermal stability of silicene", "volume": "95", "year": "2017" } ], "species": [ "Si" ], "title": "Three-body bond-order potential for Si by Purja Pun and Mishin (2017) v000", "created_on": "2023-10-07 07:51:29.299562" }, "test": "EquilibriumCrystalStructure_A_tP106_137_a5g4h_Si__TE_330658189378_003", "model": "ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000", "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_330658189378_003-and-MO_566683736730_000-1752531718-tr", "created_on": "2025-07-15 00:35:05.917533", "dependencies": [] }, "created_on": "2025-07-15 00:35:05.917533", "inserted_on": "2025-07-15 03:03:21.198572", "latest": true } ] NOTE: The configuration you provided has a maximum force component 0.2568080786151015 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 0.0010175683052783325 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]: [[ 1150002.1401 775020.28749 -113828.86776 54514.52724 54303.55983 28351.64055] [ 775020.28749 1151127.78472 297570.9515 -2301001.57541 -114611.56886 -113887.65089] [ -113828.86776 297570.9515 -69172.8084 3400.01203 -222947.74036 296074.87823] [ 54514.52724 -2301001.57541 3400.01203 113992.9177 14358.48586 -957785.38657] [ 54303.55983 -114611.56886 -222947.74036 14358.48586 72131.34168 26366.52851] [ 28351.64055 -113887.65089 296074.87823 -957785.38657 26366.52851 113979.89422]] 95%% Error estimate [ASE units]: [[ 720495.5237 3076990.22583 2753009.51876 635542.69314 1602107.3907 313207.5646 ] [ 3076990.22583 868876.91714 2623006.52291 2783951.45358 442853.98516 120582.27566] [ 2753009.51876 2623006.52291 12365942.71639 2078413.6361 1301137.31897 2625389.34713] [ 635542.69314 2783951.45358 2078413.6361 138932.90805 430294.96061 282463.13518] [ 1602107.3907 442853.98516 1301137.31897 430294.96061 1133505.90811 488288.07838] [ 313207.5646 120582.27566 2625389.34713 282463.13518 488288.07838 135206.1433 ]] Relative norm of error estimate: 3.0328741741756082 Relative norm of deviation from material symmetry: 0.9380464826555591 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]: [[29884.58272 -3170.04984 3245.73303 -289.47959 4.51385 -12.59591] [-3170.04984 30032.8348 -1247.52429 -8334.47897 204.42185 -2524.50627] [ 3245.73303 -1247.52429 35.85933 -715.5791 144.67341 368.21216] [ -289.47959 -8334.47897 -715.5791 20479.03204 405.34966 17.6732 ] [ 4.51385 204.42185 144.67341 405.34966 5195.64145 366.70184] [ -12.59591 -2524.50627 368.21216 17.6732 366.70184 2877.35966]] 95%% Error estimate [ASE units]: [[653006.98561 52458.71171 128643.53532 3119.03451 12731.25572 3800.91657] [ 52458.71171 37151.2223 66599.84834 15854.95301 10530.70254 1827.52042] [128643.53532 66599.84834 161707.69341 3318.0937 2569.36235 12302.21423] [ 3119.03451 15854.95301 3318.0937 18216.3725 7308.28923 4992.0058 ] [ 12731.25572 10530.70254 2569.36235 7308.28923 3943.24959 559.45093] [ 3800.91657 1827.52042 12302.21423 4992.0058 559.45093 1977.88985]] Relative norm of error estimate: 11.297802677431658 Relative norm of deviation from material symmetry: 0.4480295672722536 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]: [[ -0.15891 42.21008 0.27421 -18.26643 22.37549 3.50445] [ 42.21008 -188.46515 0.51106 12.4167 5.66968 3.50445] [ 0.27421 0.51106 0.81653 0.08419 0.09774 0.14846] [ -18.26643 12.4167 0.08419 -9.06135 0. -0. ] [ 22.37549 5.66968 0.09774 0. 56.32576 0. ] [ 3.50445 3.50445 0.14846 -0. 0. 50.02068]] 95%% Error estimate [ASE units]: [[ 37.87412 60.45105 3.32766 20.2929 80.44313 50.92663] [ 60.45105 202.30102 4.95789 18.00784 34.98729 50.92663] [ 3.32766 4.95789 0.72292 1.31308 1.36597 2.67172] [ 20.2929 18.00784 1.31308 8.42715 0. 0. ] [ 80.44313 34.98729 1.36597 0. 195.98401 0. ] [ 50.92663 50.92663 2.67172 0. 0. 72.04963]] Relative norm of error estimate: 2.0512972824397298 Relative norm of deviation from material symmetry: 0.6778695411465653 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]: [[-0.26401 -6.85377 0.06189 1.3641 0.34596 -0.47763] [-6.85377 -5.56659 0.06323 0.02312 -0.01308 -0.47763] [ 0.06189 0.06323 0.58918 0.00966 0.04191 0.16846] [ 1.3641 0.02312 0.00966 -1.76435 0. -0. ] [ 0.34596 -0.01308 0.04191 0. -0.77186 -0. ] [-0.47763 -0.47763 0.16846 -0. -0. 0.09308]] 95%% Error estimate [ASE units]: [[37.14076 27.26372 1.33834 1.47091 0.52334 0.52581] [27.26372 27.44317 1.16413 3.66791 2.07869 0.52581] [ 1.33834 1.16413 1.39547 0.05562 0.04184 0.31025] [ 1.47091 3.66791 0.05562 5.06285 0. 0. ] [ 0.52334 2.07869 0.04184 0. 7.47998 0. ] [ 0.52581 0.52581 0.31025 0. 0. 11.43796]] Relative norm of error estimate: 5.511956796332782 Relative norm of deviation from material symmetry: 0.41537774093358926 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]: [[ 0.59319 0.88647 0.17184 0.05571 -0.04463 -0.02767] [ 0.88647 3.42271 0.18683 -0.00171 -0.01265 -0.02767] [ 0.17184 0.18683 0.59159 -0.00084 -0.00151 -0.00986] [ 0.05571 -0.00171 -0.00084 -0.13115 -0. 0. ] [-0.04463 -0.01265 -0.00151 -0. 0.16699 0. ] [-0.02767 -0.02767 -0.00986 0. 0. 0.15251]] 95%% Error estimate [ASE units]: [[1.27108 1.00343 2.38486 0.39338 0.21025 0.43206] [1.00343 1.57678 2.38425 0.1409 0.18087 0.43206] [2.38486 2.38425 0.0249 0.01884 0.02201 0.02218] [0.39338 0.1409 0.01884 0.24881 0. 0. ] [0.21025 0.18087 0.02201 0. 0.59762 0. ] [0.43206 0.43206 0.02218 0. 0. 1.43824]] Relative norm of error estimate: 1.6927866668788134 Relative norm of deviation from material symmetry: 0.5402764841217652 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]: [[ 0.50505 0.38181 0.16173 0.00257 -0.00361 -0.01499] [ 0.38181 0.4899 0.15743 -0.00553 0.00157 -0.01499] [ 0.16173 0.15743 0.6055 0.00022 -0.001 -0.00138] [ 0.00257 -0.00553 0.00022 0.15362 -0. -0. ] [-0.00361 0.00157 -0.001 -0. 0.14896 -0. ] [-0.01499 -0.01499 -0.00138 -0. -0. 0.1572 ]] 95%% Error estimate [ASE units]: [[0.29927 0.18566 0.14534 0.01587 0.02902 0.08712] [0.18566 0.34509 0.14517 0.0849 0.02763 0.08712] [0.14534 0.14517 0.03552 0.00102 0.00224 0.00588] [0.01587 0.0849 0.00102 0.04136 0. 0. ] [0.02902 0.02763 0.00224 0. 0.06114 0. ] [0.08712 0.08712 0.00588 0. 0. 0.04817]] Relative norm of error estimate: 0.5650707930528034 Relative norm of deviation from material symmetry: 0.03778291380770082 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=0.1,_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]: [[ 0.50505 0.38181 0.16173 0.00257 -0.00361 -0.01499] [ 0.38181 0.4899 0.15743 -0.00553 0.00157 -0.01499] [ 0.16173 0.15743 0.6055 0.00022 -0.001 -0.00138] [ 0.00257 -0.00553 0.00022 0.15362 -0. -0. ] [-0.00361 0.00157 -0.001 -0. 0.14896 -0. ] [-0.01499 -0.01499 -0.00138 -0. -0. 0.1572 ]] 95%% Error estimate [ASE units]: [[0.29927 0.18566 0.14534 0.01587 0.02902 0.08712] [0.18566 0.34509 0.14517 0.0849 0.02763 0.08712] [0.14534 0.14517 0.03552 0.00102 0.00224 0.00588] [0.01587 0.0849 0.00102 0.04136 0. 0. ] [0.02902 0.02763 0.00224 0. 0.06114 0. ] [0.08712 0.08712 0.00588 0. 0. 0.04817]] Relative norm of error estimate: 0.5650707930528034 Relative norm of deviation from material symmetry: 0.03778291380770082 R E S U L T S Elastic constants [GPa]: [[80.91859 61.17333 25.91216 0.41148 -0.57817 -2.40182] [61.17333 78.49073 25.223 -0.88668 0.25176 -2.40182] [25.91216 25.223 97.01167 0.03474 -0.1595 -0.22148] [ 0.41148 -0.88668 0.03474 24.61262 -0. -0. ] [-0.57817 0.25176 -0.1595 -0. 23.86593 -0. ] [-2.40182 -2.40182 -0.22148 -0. -0. 25.1866 ]] 95 %% Error estimate [GPa]: [[47.94851 29.74656 23.28535 2.54239 4.64985 13.95822] [29.74656 55.28958 23.2584 13.60312 4.42723 13.95822] [23.28535 23.2584 5.69075 0.16418 0.35964 0.94154] [ 2.54239 13.60312 0.16418 6.6272 0. 0. ] [ 4.64985 4.42723 0.35964 0. 9.79493 0. ] [13.95822 13.95822 0.94154 0. 0. 7.71726]] Bulk modulus [GPa] = 53.002330150513 Unique elastic constants for space group 137 [GPa] ['c11', 'c12', 'c13', 'c33', 'c44', 'c66'] [79.70466212928835, 61.173327510590475, 25.56757707145957, 97.01166827968531, 24.239274112126633, 25.186598216871545] Nearest matrix of isotropic elastic constants: Distance to isotropic state [-] = 1.062020299881161 Isotropic bulk modulus [GPa] = 53.288264505929284 Isotropic shear modulus [GPa] = 22.093007137171515