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add vicker's hardness dataset #765

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Merged
ardunn merged 4 commits into from
Mar 4, 2022
Merged

add vicker's hardness dataset #765

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3fd2c52
add vicker's hardness dataset to matminer metadata
ardunn Jan 13, 2022
a5058fa
update metadata again [skip ci] [skip actions]
ardunn Jan 13, 2022
b3ea21d
Merge branch 'main' of https://github.com/hackingmaterials/matminer
ardunn Feb 8, 2022
fb7faef
try cohesive energy mp with n_jobs set to 1
ardunn Feb 8, 2022
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16 changes: 16 additions & 0 deletions matminer/datasets/dataset_metadata.json
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Original file line number Diff line number Diff line change
Expand Up @@ -935,5 +935,21 @@
"reference": "https://doi.org/10.1038/s41524-018-0085-8",
"url": "https://figshare.com/ndownloader/files/31614956",
"hash": "03976fc80f39b3d9c7bf2c12f4d045513cdad17a3a2e559f6290fc7245154418"
},
"zhang_brgoch_load_vickers_hardness": {
"bibtex_refs": [
"@article{Zhang2020,\n doi = {10.1002/adma.202005112},\n url = {https://doi.org/10.1002/adma.202005112},\n year = {2020},\n month = dec,\n publisher = {Wiley},\n volume = {33},\n number = {5},\n pages = {2005112},\n author = {Ziyan Zhang and Aria Mansouri Tehrani and Anton O. Oliynyk and Blake Day and Jakoah Brgoch},\n title = {Finding the Next Superhard Material through Ensemble Learning},\n journal = {Advanced Materials}\n}",
],
"columns":{
"composition": "Chemical formula. One chemical formula may correspond to multiple separate measurements.",
"hardness": "Vicker's load-dependent hardness, in GPa.",
"load": "Applied load, in N."
},
"description": "Dataset of 1061 experimentally measured load-dependent Vicker's hardness measurements. Multiple measurements may correspond to one composition. Collated by Zhang et al.",
"file_type": "json.gz",
"num_entries": 1061,
"reference": "https://doi.org/10.1002/adma.202005112",
"url": null,
"hash": null
}
}
5 changes: 4 additions & 1 deletion matminer/featurizers/composition/tests/test_thermo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@


class ThermoFeaturesTest(CompositionFeaturesTest):

def test_cohesive_energy(self):
mpr = MPRester()
if not mpr.api_key:
Expand All @@ -19,7 +20,9 @@ def test_cohesive_energy_mp(self):
mpr = MPRester()
if not mpr.api_key:
raise SkipTest("Materials Project API key not set; Skipping cohesive energy test")
df_cohesive_energy = CohesiveEnergyMP().featurize_dataframe(self.df, col_id="composition")
ce = CohesiveEnergyMP()
ce.set_n_jobs(1)
df_cohesive_energy = ce.featurize_dataframe(self.df, col_id="composition")
self.assertAlmostEqual(df_cohesive_energy["cohesive energy (MP)"][0], 5.778053364, 2)


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