Model name?
Sim_LAMMPS_reaxFF_FthenakisPetsalakisTozzini_2022_CHON__SM_198543900691_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)?
[
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"disclaimer": "The forces and stresses failed to converge to the requested tolerance",
"prototype-label": {
"source-value": "A11B4_tP15_111_abcmn_n"
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"stoichiometric-species": {
"source-value": [
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"a": {
"source-value": 3.5518592066615216,
"source-unit": "angstrom",
"si-unit": "m",
"si-value": 3.551859206661522e-10
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"z4",
"x5",
"z5",
"x6",
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"parameter-values": {
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2.0345310319127763,
0.7595937483661066,
0.7440726534357534,
0.6268115990127636,
0.7672937990283133,
0.1291421951627072
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"library-prototype-label": {
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"cell-cauchy-stress": {
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0.0,
0.0,
0.0,
0.0,
0.0
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"source-unit": "eV/angstrom^3",
"si-unit": "kg / m s^2",
"si-value": [
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0.0,
0.0,
0.0,
0.0
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"temperature": {
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"crystal-genome-source-structure-id": {
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"coordinates-file": {
"source-value": "instance-1.poscar"
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"coordinates-file-conventional": {
"source-value": "conventional.instance-1.poscar"
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"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.",
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"properties": [
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"publication-year": "2025",
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{
"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 CN in AFLOW crystal prototype A11B4_tP15_111_abcmn_n v003",
"test-driver": "EquilibriumCrystalStructure__TD_457028483760_003",
"species": [
"C",
"N"
],
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"description": "Computes the equilibrium crystal structure and energy for CN in AFLOW crystal prototype A11B4_tP15_111_abcmn_n 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): 3.5402, c/a: 1.9769787, z4: 0.75672018, x5: 0.25529038, z5: 0.62795568, x6: 0.23666293, z6: 0.12490625, obtained from OpenKIM Reference Data item RD_235830653357_000",
"disclaimer": "Computer generated",
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"maintainer-id": "4ad03136-ed7f-4316-b586-1e94ccceb311",
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"created_on": "2025-07-22 18:12:37.900048"
},
"subject": {
"extended-id": "Sim_LAMMPS_reaxFF_FthenakisPetsalakisTozzini_2022_CHON__SM_198543900691_000",
"short-id": "SM_198543900691_000",
"kimid-prefix": "Sim_LAMMPS_reaxFF_FthenakisPetsalakisTozzini_2022_CHON",
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"name": "Sim_LAMMPS_reaxFF_FthenakisPetsalakisTozzini_2022_CHON",
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"content-origin": "Fthenakis Z.G., Petsalakis I.D., Tozzini V. and Lathiotakis N.N., Front. Chem. 10, 951261 (2022)\n\nhttps://www.frontiersin.org/articles/10.3389/fchem.2022.951261",
"contributor-id": "37d54f8e-3238-4542-b985-230fc95d5045",
"description": "The here described potential belongs to the type of Reax potentials, which is designed to describe interactions between condensed carbon phases (like graphene, diamond etc) and molecules composed of C, H, O and/or N atoms. It is a hybrid potential combining two other Reax potentials, namely the C-2013 potential (Srinivasan, S. G., van Duin, A. C. T., and Ganesh, P., J. Phys. Chem. A 119, 571\u2013580 (2015)) for carbon condensed phases and RDX potential (Strachan, A., van Duin, A. C. T., Chakraborty, D., Dasgupta, S., and Goddard, W. A., Phys. Rev. Lett. 91, 098301 (2003)) for interactions between C/H/O/N atoms and molecules composed of C/H/O/N atoms, originally designed to describe initial chemical events in nitramine RDX explosions. The potential considers a hypothetical new species denoted as Cg, representing the carbon atoms in condensed carbon phases, and C, representing the carbon atoms in all other cases. The interactions between C/H/O/N atoms are described by the RDX potential, while the interactions between Cg-Cg atoms are described by a slightly modified C-2013 potential. Moreover, the interactions between Cg-C, Cg-H, Cg-O and Cg-N are also described by RDX potential, as if Cg was a C atom. The modification of GR-RDX-2021 potential with respect to the C-2013 for the Cg-Cg interactions has to do with the 39 general parameters of the potential, which has been chosen to be the parameters of the RDX potential.",
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"disclaimer": "The applicability of the potential for 3-fold coordinated C systems, in comparison with other ReaxFFs can be found in Fthenakis Z.G., Petsalakis I.D., Tozzini V. and Lathiotakis N.N., Front. Chem. 10, 951261 (2022), \nhttps://www.frontiersin.org/articles/10.3389/fchem.2022.951261",
"doi": "10.25950/88a246c7",
"domain": "openkim.org",
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"funding": [
{
"award-number": "952068",
"award-title": "EU-H2020 FETPROACT LESGO ",
"funder-name": "European Union"
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{
"award-title": "MONSTRE-2D PRIN2017 KFMJ8E",
"funder-name": "Italian Ministry of University and Research"
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{
"award-number": "MIS 5041612",
"award-title": "nanoporous GrAphene membrane made without Transfer for gas Separation\u2013GATES",
"funder-identifier": "https://doi.org/10.13039/501100008530",
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"funder-name": "European Regional Development Fund",
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}
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"maintainer-id": "37d54f8e-3238-4542-b985-230fc95d5045",
"potential-type": "reax",
"publication-year": "2023",
"run-compatibility": "portable-models",
"simulator-name": "LAMMPS",
"simulator-potential": "reaxff",
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{
"abstract": "We study the performance of eleven reactive force fields (ReaxFF), which can be used to study sp2 carbon systems. Among them a new hybrid ReaxFF is proposed combining two others and introducing two different types of C atoms. The advantages of that potential are discussed. We analyze the behavior of ReaxFFs with respect to 1) the structural and mechanical properties of graphene, its response to strain and phonon dispersion relation; 2) the energetics of (n, 0) and (n, n) carbon nanotubes (CNTs), their mechanical properties and response to strain up to fracture; 3) the energetics of the icosahedral C60 fullerene and the 40 C40 fullerene isomers. Seven of them provide not very realistic predictions for graphene, which made us focusing on the remaining, which provide reasonable results for 1) the structure, energy and phonon band structure of graphene, 2) the energetics of CNTs versus their diameter and 3) the energy of C60 and the trend of the energy of the C40 fullerene isomers versus their pentagon adjacencies, in accordance with density functional theory (DFT) calculations and/or experimental data. Moreover, the predicted fracture strain, ultimate tensile strength and strain values of CNTs are inside the range of experimental values, although overestimated with respect to DFT. However, they underestimate the Young\u2019s modulus, overestimate the Poisson\u2019s ratio of both graphene and CNTs and they display anomalous behavior of the stress - strain and Poisson\u2019s ratio - strain curves, whose origin needs further investigation.",
"author": "Fthenakis, Zacharias G. and Petsalakis, Ioannis D. and Tozzini, Valentina and Lathiotakis, Nektarios N.",
"doi": "10.3389/fchem.2022.951261",
"issn": "2296-2646",
"journal": "Frontiers in Chemistry",
"recordkey": "SM_198543900691_000a",
"recordtype": "article",
"title": "Evaluating the performance of ReaxFF potentials for sp2 carbon systems (graphene, carbon nanotubes, fullerenes) and a new ReaxFF potential",
"url": "https://www.frontiersin.org/articles/10.3389/fchem.2022.951261",
"volume": "10",
"year": "2022"
}
],
"species": [
"C",
"H",
"O",
"N"
],
"title": "LAMMPS ReaxFF potential for C-H-N-O systems developed by Fthenakis et al. (2022) v001",
"created_on": "2023-11-29 17:24:32.899472"
},
"test": "EquilibriumCrystalStructure_A11B4_tP15_111_abcmn_n_CN__TE_507095464218_003",
"simulator-model": "Sim_LAMMPS_reaxFF_FthenakisPetsalakisTozzini_2022_CHON__SM_198543900691_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_507095464218_003-and-SM_198543900691_000-1753208205-tr",
"created_on": "2025-07-23 04:30:09.358432",
"dependencies": []
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"created_on": "2025-07-23 04:30:09.358432",
"inserted_on": "2025-07-23 04:34:37.605371",
"latest": true
}
]
NOTE: The configuration you provided has a maximum force component 0.009244315880695977 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 0.0014089050465178874 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]:
[[ 14.01213 15.13017 14.72191 -16.55775 0.02582 -2.73875]
[ 15.13017 14.46808 14.00405 0.08823 -0.11252 0.58988]
[ 14.72191 14.00405 16.38726 11.12737 -0.43363 -0.0172 ]
[-16.55775 0.08823 11.12737 2.81602 -0.05144 -0.02456]
[ 0.02582 -0.11252 -0.43363 -0.05144 2.62813 -0.02757]
[ -2.73875 0.58988 -0.0172 -0.02456 -0.02757 1.55832]]
95%% Error estimate [ASE units]:
[[ 5.1098 11.8415 5.14321 38.3453 0.8641 4.00894]
[ 11.8415 7.78595 2.56665 0.2654 122.6303 1.08239]
[ 5.14321 2.56665 84.78743 9.23277 314.0354 0.09631]
[ 38.3453 0.2654 9.23277 0.12515 0.78088 0.06732]
[ 0.8641 122.6303 314.0354 0.78088 0.29933 0.29042]
[ 4.00894 1.08239 0.09631 0.06732 0.29042 2.20564]]
Relative norm of error estimate: 11.328735602281693
Relative norm of deviation from material symmetry: 0.667119375291269
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]:
[[19.8683 17.01774 17.10729 -0.27875 0.0665 0.00379]
[17.01774 19.21249 17.12514 2.17242 -0.00717 -0.0002 ]
[17.10729 17.12514 20.70583 -0.05352 0.01249 0.02328]
[-0.27875 2.17242 -0.05352 2.75629 0.00214 -0.07512]
[ 0.0665 -0.00717 0.01249 0.00214 2.60826 -0.00049]
[ 0.00379 -0.0002 0.02328 -0.07512 -0.00049 2.87665]]
95%% Error estimate [ASE units]:
[[ 1.07583 0.21893 0.31363 6.94764 0.11155 0.00511]
[ 0.21893 0.69635 0.22694 2.73637 0.02565 0.01503]
[ 0.31363 0.22694 0.07765 9.92548 0.0472 0.14854]
[ 6.94764 2.73637 9.92548 0.08686 37.32191 0.64678]
[ 0.11155 0.02565 0.0472 37.32191 0.37069 0.00193]
[ 0.00511 0.01503 0.14854 0.64678 0.00193 0.0037 ]]
Relative norm of error estimate: 1.9629862372054367
Relative norm of deviation from material symmetry: 0.07996260369319391
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]:
[[26.61197 23.72475 23.72567 -0.0025 -0.01616 0.01143]
[23.72475 26.38234 23.58183 -0.00251 -0.01183 0.01143]
[23.72567 23.58183 27.11217 -0.0025 -0.01446 -0.001 ]
[-0.0025 -0.00251 -0.0025 2.76684 0. -0. ]
[-0.01616 -0.01183 -0.01446 0. 2.76706 -0. ]
[ 0.01143 0.01143 -0.001 -0. -0. 2.9348 ]]
95%% Error estimate [ASE units]:
[[0.36394 0.94675 4.46462 0.03617 0.06282 0.0128 ]
[0.94675 1.88051 4.55414 0.03175 0.05784 0.0128 ]
[4.46462 4.55414 6.13943 0.03344 0.05906 0.02017]
[0.03617 0.03175 0.03344 0.00754 0. 0. ]
[0.06282 0.05784 0.05906 0. 0.00206 0. ]
[0.0128 0.0128 0.02017 0. 0. 0.00687]]
Relative norm of error estimate: 0.14915728536225775
Relative norm of deviation from material symmetry: 0.003007001549836606
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]:
[[19.81693 16.75183 17.06315 0.00769 0.05571 0.00013]
[16.75183 19.81989 17.06471 0.00713 0.05331 0.00013]
[17.06315 17.06471 20.89121 0.01496 -0.01946 0.00009]
[ 0.00769 0.00713 0.01496 2.75174 0. -0. ]
[ 0.05571 0.05331 -0.01946 0. 2.75221 -0. ]
[ 0.00013 0.00013 0.00009 -0. -0. 2.87251]]
95%% Error estimate [ASE units]:
[[0.03153 0.02111 2.2561 0.01611 0.01658 0.00116]
[0.02111 0.02978 2.25605 0.01922 0.02639 0.00116]
[2.2561 2.25605 5.04789 0.0201 0.05205 0.00103]
[0.01611 0.01922 0.0201 0.03154 0. 0. ]
[0.01658 0.02639 0.05205 0. 0.00233 0. ]
[0.00116 0.00116 0.00103 0. 0. 0.00219]]
Relative norm of error estimate: 0.12280015345478242
Relative norm of deviation from material symmetry: 0.0029596542280036268
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]:
[[20.78988 17.30487 17.50024 -0.0001 0.00971 0.00105]
[17.30487 19.94996 17.06217 0.00002 0.00969 0.00105]
[17.50024 17.06217 20.66753 0.00013 0.02532 0.00155]
[-0.0001 0.00002 0.00013 2.7516 -0. -0. ]
[ 0.00971 0.00969 0.02532 -0. 2.753 -0. ]
[ 0.00105 0.00105 0.00155 -0. -0. 2.87596]]
95%% Error estimate [ASE units]:
[[ 5.3257 7.63599 2.90584 0.01533 0.02541 0.00323]
[ 7.63599 14.44477 8.01717 0.01281 0.02579 0.00323]
[ 2.90584 8.01717 1.69712 0.02292 0.02686 0.00429]
[ 0.01533 0.01281 0.02292 0.00637 0. 0. ]
[ 0.02541 0.02579 0.02686 0. 0.00271 0. ]
[ 0.00323 0.00323 0.00429 0. 0. 0.00213]]
Relative norm of error estimate: 0.39952489356163334
Relative norm of deviation from material symmetry: 0.013200490227646074
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]:
[[19.81581 16.74994 16.98603 0.00033 -0.00002 0.00011]
[16.74994 19.81588 16.98611 0.00031 0.00001 0.00011]
[16.98603 16.98611 20.68587 0.00041 0.00006 0.00011]
[ 0.00033 0.00031 0.00041 2.74966 0. 0. ]
[-0.00002 0.00001 0.00006 0. 2.75404 -0. ]
[ 0.00011 0.00011 0.00011 0. -0. 2.87485]]
95%% Error estimate [ASE units]:
[[1.59727 1.09985 1.17955 0.00124 0.00398 0.00082]
[1.09985 1.43963 1.10685 0.00128 0.0039 0.00082]
[1.17955 1.10685 1.33442 0.00123 0.00437 0.00056]
[0.00124 0.00128 0.00123 0.02229 0. 0. ]
[0.00398 0.0039 0.00437 0. 0.00925 0. ]
[0.00082 0.00082 0.00056 0. 0. 0.00152]]
Relative norm of error estimate: 0.06820668924653912
Relative norm of deviation from material symmetry: 0.00011485490634496522
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]:
[[19.81581 16.74994 16.98603 0.00033 -0.00002 0.00011]
[16.74994 19.81588 16.98611 0.00031 0.00001 0.00011]
[16.98603 16.98611 20.68587 0.00041 0.00006 0.00011]
[ 0.00033 0.00031 0.00041 2.74966 0. 0. ]
[-0.00002 0.00001 0.00006 0. 2.75404 -0. ]
[ 0.00011 0.00011 0.00011 0. -0. 2.87485]]
95%% Error estimate [ASE units]:
[[1.59727 1.09985 1.17955 0.00124 0.00398 0.00082]
[1.09985 1.43963 1.10685 0.00128 0.0039 0.00082]
[1.17955 1.10685 1.33442 0.00123 0.00437 0.00056]
[0.00124 0.00128 0.00123 0.02229 0. 0. ]
[0.00398 0.0039 0.00437 0. 0.00925 0. ]
[0.00082 0.00082 0.00056 0. 0. 0.00152]]
Relative norm of error estimate: 0.06820668924653912
Relative norm of deviation from material symmetry: 0.00011485490634496522
R E S U L T S
Elastic constants [GPa]:
[[3174.84348 2683.63659 2721.46204 0.05227 -0.00317 0.0177 ]
[2683.63659 3174.85368 2721.47433 0.04962 0.001 0.0177 ]
[2721.46204 2721.47433 3314.24143 0.06571 0.01038 0.01797]
[ 0.05227 0.04962 0.06571 440.54477 0. 0. ]
[ -0.00317 0.001 0.01038 0. 441.24507 -0. ]
[ 0.0177 0.0177 0.01797 0. -0. 460.60214]]
95 %% Error estimate [GPa]:
[[255.91118 176.21583 188.98475 0.19798 0.63713 0.1315 ]
[176.21583 230.6537 177.33722 0.20428 0.62445 0.1315 ]
[188.98475 177.33722 213.79722 0.19706 0.70087 0.08965]
[ 0.19798 0.20428 0.19706 3.57125 0. 0. ]
[ 0.63713 0.62445 0.70087 0. 1.48234 0. ]
[ 0.1315 0.1315 0.08965 0. 0. 0.24333]]
Bulk modulus [GPa] = 2875.316741683338
Unique elastic constants for space group 111 [GPa]
['c11', 'c12', 'c13', 'c33', 'c44', 'c66']
[3174.8485804042916, 2683.636590713336, 2721.4681855240206, 3314.2414311957245, 440.8949199568261, 460.6021439767131]
Nearest matrix of isotropic elastic constants:
Distance to isotropic state [-] = 0.6172628260669519
Isotropic bulk modulus [GPa] = 2879.1110498965686
Isotropic shear modulus [GPa] = 357.7549141985037