{"id":5911,"date":"2024-08-27T10:01:03","date_gmt":"2024-08-27T02:01:03","guid":{"rendered":""},"modified":"2024-08-27T10:01:03","modified_gmt":"2024-08-27T02:01:03","slug":"\u8bfb\u53d6mat\u6570\u636e_matlab\u5b58\u50a8\u6570\u636e\u4e3amat","status":"publish","type":"post","link":"https:\/\/mushiming.com\/5911.html","title":{"rendered":"\u8bfb\u53d6mat\u6570\u636e_matlab\u5b58\u50a8\u6570\u636e\u4e3amat"},"content":{"rendered":"

\n <\/path> \n<\/svg> <\/p>\n

materials project<\/h3>\n

matminer.data_retrieval.retrieve_MP.MPDataRetrieval\u83b7\u53d6materials project\u6570\u636e<\/p>\n

from<\/span> matminer.<\/span>data_retrieval.<\/span>retrieve_MP import<\/span> MPDataRetrieval <\/code><\/pre>\n
mpdr=<\/span>MPDataRetrieval(<\/span>api_key=<\/span>''<\/span>)<\/span> <\/code><\/pre>\n
#\u5f97\u5230\u6240\u6709\u5143\u7d20\u6750\u6599\u7684\u5bc6\u5ea6\uff0c\u4f8b\u5982\u90a3\u4e9b\u5305\u542b\u4e00\u4e2a\u5143\u7d20\u7684\u6750\u6599<\/span> df=<\/span>mpdr.<\/span>get_dataframe(<\/span>criteria=<\/span>{ \n   <\/span>'nelements'<\/span>:<\/span>1<\/span>}<\/span>,<\/span> properties=<\/span>[<\/span>'density'<\/span>,<\/span>'pretty_formula'<\/span>]<\/span>)<\/span> print<\/span>(<\/span>'there are %d enties on MP with 1 element'<\/span>%<\/span>(<\/span>df[<\/span>'density'<\/span>]<\/span>.<\/span>count(<\/span>)<\/span>)<\/span>)<\/span> <\/code><\/pre>\n
 0%| | 0\/716 [00:00<?, ?it\/s] there are 716 enties on MP with 1 element <\/code><\/pre>\n
\u83b7\u53d6\u5927\u4e8e4.0 eV \u7684\u6240\u6709\u5e26\u9699<\/h4>\n
\u5e26\u9699\u662f band_gap\uff0c\u5927\u4e8e4\u600e\u4e48\u5199\u5462\uff1f \u662f greater than, \u7f29\u5199\u6210 gt. \u5728 matminer\u4e2d\u8981\u5199\u6210 $gt<\/p>\n
df=<\/span>mpdr.<\/span>get_dataframe(<\/span>{ \n   <\/span>'band_gap'<\/span>:<\/span>{ \n   <\/span>'$gt'<\/span>:<\/span>4.0<\/span>}<\/span>}<\/span>,<\/span>[<\/span>'pretty_formula'<\/span>,<\/span>'band_gap'<\/span>]<\/span>)<\/span> <\/code><\/pre>\n
 0%| | 0\/8285 [00:00<?, ?it\/s] <\/code><\/pre>\n
df.<\/span>head(<\/span>)<\/span> <\/code><\/pre>\n
\n<\/div>\n\n\n\n\n\n\n\n\n\n\n
<\/th>\npretty_formula<\/th>\nband_gap<\/th>\n<\/tr>\n
material_id<\/th>\n<\/th>\n<\/th>\n<\/tr>\n<\/thead>\n
mp-10080<\/th>\nPrGeBO5<\/td>\n4.0136<\/td>\n<\/tr>\n
mp-<\/th>\nNa2Ge(S2O7)3<\/td>\n4.0030<\/td>\n<\/tr>\n
mp-<\/th>\nKLiICl<\/td>\n4.0542<\/td>\n<\/tr>\n
mp-<\/th>\nCsC2N3<\/td>\n4.0731<\/td>\n<\/tr>\n
mp-<\/th>\nY2(CN2)3<\/td>\n4.0364<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Get all VRH shear and bulk moduli from the \u201celasticity\u201d sub-document for which no warnings are found<\/h4>\n
\u5b58\u5728\u5f39\u6027\u5e38\u6570\u4fe1\u606f\u7528 \u201celasticity\u201d: {\u201c$exists\u201d: True} \u8868\u793a\uff0c\u6ca1\u6709\u8b66\u544a\u4fe1\u606f\u7528 \u4e00\u4e2a\u7a7a\u5217\u8868\u8868\u793a\uff1a\u201celasticity.warnings\u201d: []<\/p>\n
df=<\/span>mpdr.<\/span>get_dataframe(<\/span>{ \n   <\/span>\"elasticity\"<\/span>:<\/span>{ \n   <\/span>\"$exists\"<\/span>:<\/span>True<\/span>}<\/span>}<\/span>,<\/span> [<\/span>'pretty_formula'<\/span>,<\/span>'elasticity.K_VRH'<\/span>,<\/span>'elasticity.G_VRH'<\/span>]<\/span>)<\/span> <\/code><\/pre>\n
 0%| | 0\/13172 [00:00<?, ?it\/s] <\/code><\/pre>\n
df.<\/span>head(<\/span>)<\/span> <\/code><\/pre>\n
\n<\/div>\n\n\n\n\n\n\n\n\n\n\n
<\/th>\npretty_formula<\/th>\nelasticity.K_VRH<\/th>\nelasticity.G_VRH<\/th>\n<\/tr>\n
material_id<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/tr>\n<\/thead>\n
mp-10003<\/th>\nNb4CoSi<\/td>\n191.0<\/td>\n97.0<\/td>\n<\/tr>\n
mp-<\/th>\nBeC2<\/td>\n83.0<\/td>\n45.0<\/td>\n<\/tr>\n
mp-<\/th>\nZrB6<\/td>\n179.0<\/td>\n41.0<\/td>\n<\/tr>\n
mp-<\/th>\nHfIr<\/td>\n202.0<\/td>\n12.0<\/td>\n<\/tr>\n
mp-<\/th>\nSiC<\/td>\n241.0<\/td>\n176.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u9664\u4e86\u4e0a\u6b21\u7684\u641c\u7d22\u6761\u4ef6\u5916\uff0c\u6211\u4eec\u60f3\u641c\u7d22\u5305\u542b Pb \u548c Te \u7684\u6750\u6599\uff1a\u201celements\u201d: {\u201cKaTeX parse error: Expected '}', got 'EOF' at end of input: \u2026above_hull\": {\"<\/span>lt\u201d: 1e-6}<\/p>\n
df=<\/span>mpdr.<\/span>get_dataframe(<\/span>criteria=<\/span>{ \n   <\/span>\"elasticity\"<\/span>:<\/span>{ \n   <\/span>'$exists'<\/span>:<\/span>True<\/span>}<\/span>,<\/span> 'elasticity.warnings'<\/span>:<\/span>[<\/span>]<\/span>,<\/span> 'elements'<\/span>:<\/span>{ \n   <\/span>'$all'<\/span>:<\/span>[<\/span>'Pb'<\/span>,<\/span>'Te'<\/span>]<\/span>}<\/span>,<\/span> 'e_above_hull'<\/span>:<\/span>{ \n   <\/span>'$lt'<\/span>:<\/span>1e-6<\/span>}<\/span>}<\/span>,<\/span> properties=<\/span>[<\/span>'elasticity.K_VRH'<\/span>,<\/span>'elasticity.G_VRH'<\/span>,<\/span> 'pretty_formula'<\/span>,<\/span>'e_above_hull'<\/span>,<\/span>'bandstructure'<\/span>,<\/span> 'dos'<\/span>]<\/span>)<\/span> <\/code><\/pre>\n
\u8ba9\u6211\u4eec\u6765\u770b\u770b
 \u8fd9\u4e9b\u7a33\u5b9a\u5316\u5408\u7269\u7684\u80fd\u5e26\u7ed3\u6784\u548c\u72b6\u6001\u5bc6\u5ea6\uff0c\u8fd9\u4e9b\u5316\u5408\u7269\u5305\u542b Pb \u548c Te\uff0c\u8fd9\u5bf9\u70ed\u7535\u5b66\u5e94\u7528\u5f88\u6709\u610f\u601d:<\/p>\n
df.<\/span>head(<\/span>)<\/span> <\/code><\/pre>\n
\n<\/div>\n\n\n\n\n\n\n\n\n
<\/th>\nelasticity.K_VRH<\/th>\nelasticity.G_VRH<\/th>\npretty_formula<\/th>\ne_above_hull<\/th>\nbandstructure<\/th>\ndos<\/th>\n<\/tr>\n
material_id<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/tr>\n<\/thead>\n
mp-19717<\/th>\n40.0<\/td>\n24.0<\/td>\nTePb<\/td>\n0<\/td>\n<pymatgen.electronic_structure.bandstructure.B...<\/td>\nComplete DOS for Full Formula (Te1 Pb1)\\nReduc...<\/td>\n<\/tr>\n
mp-20740<\/th>\n25.0<\/td>\n13.0<\/td>\nTl4Te3Pb<\/td>\n0<\/td>\n<pymatgen.electronic_structure.bandstructure.B...<\/td>\nComplete DOS for Full Formula (Tl8 Te6 Pb2)\\nR...<\/td>\n<\/tr>\n
mp-<\/th>\n34.0<\/td>\n16.0<\/td>\nTe2Pd3Pb2<\/td>\n0<\/td>\n<pymatgen.electronic_structure.bandstructure.B...<\/td>\nComplete DOS for Full Formula (Te4 Pd6 Pb4)\\nR...<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
from<\/span> pymatgen.<\/span>electronic_structure.<\/span>plotter import<\/span> BSDOSPlotter <\/code><\/pre>\n
E:\\Anaconda\\lib\\site-packages\\pymatgen\\electronic_structure\\boltztrap.py:58: FutureWarning: which is deprecated; use which in shutil instead. shutil.which has been available since Python 3.3. This will be removed in v2023. which(\"x_trans\"), <\/code><\/pre>\n
mpid=<\/span>'mp-20740'<\/span> idx=<\/span>df.<\/span>index[<\/span>df.<\/span>index==<\/span>mpid]<\/span>[<\/span>0<\/span>]<\/span> <\/code><\/pre>\n
import<\/span> matplotlib.<\/span>pyplot as<\/span> plt BSDOSPlotter(<\/span>)<\/span>.<\/span>get_plot(<\/span>bs=<\/span>df.<\/span>loc[<\/span>mpid,<\/span>'bandstructure'<\/span>]<\/span>,<\/span>dos=<\/span>df.<\/span>loc[<\/span>mpid,<\/span>'dos'<\/span>]<\/span>)<\/span> plt.<\/span>show(<\/span>)<\/span> <\/code><\/pre>\n
\"\u8bfb\u53d6mat\u6570\u636e_matlab\u5b58\u50a8\u6570\u636e\u4e3amat<\/p>\n
Citrine informatics<\/h3>\n
from<\/span> matminer.<\/span>data_retrieval.<\/span>retrieve_Citrine import<\/span> CitrineDataRetrieval <\/code><\/pre>\n
cdr=<\/span>CitrineDataRetrieval(<\/span>api_key=<\/span>''<\/span>)<\/span> <\/code><\/pre>\n
df=<\/span>cdr.<\/span>get_dataframe(<\/span>criteria=<\/span>{ \n   <\/span>'formula'<\/span>:<\/span>'Si'<\/span>,<\/span> 'data_type'<\/span>:<\/span>'EXPERIMENTAL'<\/span>}<\/span>,<\/span> properties=<\/span>[<\/span>'Bnad gap'<\/span>]<\/span>,<\/span> secondary_fields=<\/span>True<\/span>)<\/span> <\/code><\/pre>\n
0it [00:00, ?it\/s] all available fields: [] suggested common fields: [] <\/code><\/pre>\n
 cdr.<\/span>get_dataframe? <\/code><\/pre>\n
\u5f97\u5230 O * \u548c OH * \u7684\u5438\u9644\u80fd<\/h4>\n
df_OH=<\/span>cdr.<\/span>get_dataframe(<\/span>criteria=<\/span>{ \n   <\/span>}<\/span>,<\/span>properties=<\/span>[<\/span>'adsorption energy of OH'<\/span>]<\/span>,<\/span> secondary_fields=<\/span>True<\/span>)<\/span> <\/code><\/pre>\n
 0%| | 0\/9 [00:00<?, ?it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 11%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 1\/9 [00:00<00:01, 5.39it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 44%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 4\/9 [00:00<00:00, 15.13it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 7\/9 [00:00<00:00, 19.22it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9\/9 [00:00<00:00, 17.45it\/s] all available fields: ['category', 'chemicalFormula', 'Adsorption energy of OH', 'references', 'uid', 'Morphology', 'Adsorption energy of OH-units', 'Adsorption energy of OH-conditions', 'Surface facet', 'Adsorption energy of OH-dataType'] suggested common fields: ['references', 'chemicalFormula', 'Surface facet', 'Adsorption energy of OH', 'Adsorption energy of OH-units', 'Adsorption energy of OH-dataType', 'Morphology', 'Adsorption energy of OH-conditions'] <\/code><\/pre>\n
df_O=<\/span>cdr.<\/span>get_dataframe(<\/span>criteria=<\/span>{ \n   <\/span>}<\/span>,<\/span>properties=<\/span>[<\/span>'adsorption energy of O'<\/span>]<\/span>,<\/span> secondary_fields=<\/span>True<\/span>)<\/span> <\/code><\/pre>\n
 0%| | 0\/21 [00:00<?, ?it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 14%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 3\/21 [00:00<00:00, 28.91it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 29%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 6\/21 [00:00<00:00, 27.23it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 43%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 9\/21 [00:00<00:00, 24.81it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 57%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 12\/21 [00:00<00:00, 25.41it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 15\/21 [00:00<00:00, 25.99it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 18\/21 [00:00<00:00, 25.90it\/s]E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:123: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead. system_normdf = json_normalize(system_value) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:129: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. non_prop_df = non_prop_df.append(non_prop_row) E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_Citrine.py:167: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. prop_df = prop_df.append(p_df) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 21\/21 [00:00<00:00, 25.96it\/s] all available fields: ['Adsorption energy of O-conditions', 'Reconstruction', 'category', 'chemicalFormula', 'Adsorption energy of O', 'references', 'uid', 'Adsorption energy of O-units', 'Surface facet'] suggested common fields: ['references', 'chemicalFormula', 'Surface facet', 'Adsorption energy of O', 'Adsorption energy of O-units', 'Adsorption energy of O-conditions', 'Reconstruction'] <\/code><\/pre>\n
df_OH.<\/span>head(<\/span>)<\/span> <\/code><\/pre>\n
\n<\/div>\n\n\n\n\n\n\n\n\n\n
<\/th>\nreferences<\/th>\nchemicalFormula<\/th>\nSurface facet<\/th>\nAdsorption energy of OH<\/th>\nAdsorption energy of OH-units<\/th>\nAdsorption energy of OH-dataType<\/th>\nMorphology<\/th>\nAdsorption energy of OH-conditions<\/th>\n<\/tr>\n<\/thead>\n
1<\/th>\n[{'citation': '10.1039\/c2cc30281k', 'doi': '10...<\/td>\nPt<\/td>\n(111)<\/td>\n2.44<\/td>\neV<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
2<\/th>\n[{'citation': '10.1016\/s1872-2067(12)60642-1',...<\/td>\nCu<\/td>\n(211)<\/td>\n-3.55<\/td>\neV<\/td>\nCOMPUTATIONAL<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
3<\/th>\n[{'citation': '10.1016\/s1872-2067(12)60642-1',...<\/td>\nZnO<\/td>\nNaN<\/td>\n-3.03<\/td>\neV<\/td>\nCOMPUTATIONAL<\/td>\nThin film<\/td>\nNaN<\/td>\n<\/tr>\n
4<\/th>\n[{'citation': '10.1016\/j.corsci.2012.11.011', ...<\/td>\nFe<\/td>\n(100)<\/td>\n-3.95<\/td>\neV<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
5<\/th>\n[{'citation': '10.1021\/jpm', 'doi': '10....<\/td>\nPt<\/td>\n(111)<\/td>\n2.71<\/td>\neV<\/td>\nNaN<\/td>\nNaN<\/td>\n[{'name': 'Site', 'scalars': [{'value': 'Top s...<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
df_O.<\/span>head(<\/span>)<\/span> <\/code><\/pre>\n
\n<\/div>\n\n\n\n\n\n\n\n\n\n
<\/th>\nreferences<\/th>\nchemicalFormula<\/th>\nSurface facet<\/th>\nAdsorption energy of O<\/th>\nAdsorption energy of O-units<\/th>\nAdsorption energy of O-conditions<\/th>\nReconstruction<\/th>\n<\/tr>\n<\/thead>\n
1<\/th>\n[{'citation': '10.1016\/j.jcat.2007.04.018', 'd...<\/td>\nFe<\/td>\n(111)<\/td>\n-5.42<\/td>\neV<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
2<\/th>\n[{'citation': '10.1002\/cctc.', 'doi':...<\/td>\nPt<\/td>\n(111)<\/td>\n1.53<\/td>\neV<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
3<\/th>\n[{'citation': '10.1021\/jpj', 'doi': '10....<\/td>\nPt<\/td>\n(111)<\/td>\n-4.54<\/td>\neV<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
4<\/th>\n[{'citation': '10.1021\/jpq', 'doi': '10....<\/td>\nCo<\/td>\n(0001)<\/td>\n2.37<\/td>\neV<\/td>\n[{'name': 'Site', 'scalars': [{'value': 'FCC s...<\/td>\nNaN<\/td>\n<\/tr>\n
5<\/th>\n[{'citation': '10.1007\/bf00', 'doi': '10...<\/td>\nRh<\/td>\n(110)<\/td>\n-300<\/td>\nkJ\/mol<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
MPDS - The Materials Platform for Data Science<\/h3>\n
from<\/span> matminer.<\/span>data_retrieval.<\/span>retrieve_MPDS import<\/span> MPDSDataRetrieval <\/code><\/pre>\n
E:\\Anaconda\\lib\\site-packages\\matminer\\data_retrieval\\retrieve_MPDS.py:30: UserWarning: No module named 'jmespath' warnings.warn(str(ex)) <\/code><\/pre>\n
MDF - The Materials Data Facility<\/h3>\n
MDF \u6570\u636e\u68c0\u7d22\u5de5\u5177 matmin.data _ review\u3002matminer.data_retrieval.retrieve_MDF.MDFDataRetrieval\u4f7f\u7528 Globus \u521d\u59cb\u5316\u952e\u8fdb\u884c\u521d\u59cb\u5316\u3002\u5728\u7b2c\u4e00\u6b21\u8c03\u7528 MDFDataRetrieval \u5bf9\u8c61\u65f6\uff0c\u5e94\u8be5\u4f1a\u63d0\u793a\u60a8\u8f93\u5165\u4e00\u4e32\u6570\u5b57\u548c\u5b57\u6bcd\uff0c\u60a8\u53ef\u4ee5\u5728 MDF Globus \u8eab\u4efd\u9a8c\u8bc1\u7f51\u7ad9\u4e0a\u8f93\u5165\u8fd9\u4e9b\u6570\u5b57\u548c\u5b57\u6bcd\u3002\u8fd9\u4e2a\u7cfb\u7edf\u7684\u4e00\u4e2a\u4f18\u70b9\u662f\u5b83\u5b9e\u9645\u4e0a\u6839\u672c\u4e0d\u9700\u8981\u8eab\u4efd\u9a8c\u8bc1\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528 onymous = True\uff0c\u5e76\u4e14\u53ef\u4ee5\u4f7f\u7528\u51e0\u4e2a MDF \u6570\u636e\u96c6\u3002\u4f46\u662f\uff0c\u5176\u4e2d\u8bb8\u591a\u4e0d\u4f1a\uff0c\u60a8\u5fc5\u987b\u4f7f\u7528 Web \u8fdb\u884c\u8eab\u4efd\u9a8c\u8bc1\u624d\u80fd\u8bbf\u95ee\u6574\u4e2a MDF\u3002<\/p>\n
from<\/span> matminer.<\/span>data_retrieval.<\/span>retrieve_MDF import<\/span> MDFDataRetrieval <\/code><\/pre>\n
mdf_dr =<\/span> MDFDataRetrieval(<\/span>anonymous=<\/span>True<\/span>)<\/span> <\/code><\/pre>\n
df=<\/span>mdf_dr.<\/span>get_dataframe(<\/span>criteria=<\/span>{ \n   <\/span>'elements'<\/span>:<\/span>[<\/span>'Ag'<\/span>,<\/span>'Be'<\/span>]<\/span>,<\/span>'sources'<\/span>:<\/span>[<\/span>'oqmd'<\/span>]<\/span>}<\/span>)<\/span> <\/code><\/pre>\n
df.<\/span>head(<\/span>)<\/span> <\/code><\/pre>\n
\n<\/div>\n\n\n\n\n\n\n\n\n\n
<\/th>\ncrystal_structure.number_of_atoms<\/th>\ncrystal_structure.space_group_number<\/th>\ncrystal_structure.volume<\/th>\ndft.converged<\/th>\ndft.cutoff_energy<\/th>\ndft.exchange_correlation_functional<\/th>\nfiles.0.data_type<\/th>\nfiles.0.filename<\/th>\nfiles.0.globus<\/th>\nfiles.0.length<\/th>\n...<\/th>\njarvis.formation_enthalpy<\/th>\njarvis.id<\/th>\njarvis.landing_page<\/th>\njarvis.total_energy<\/th>\norigin.creator<\/th>\norigin.name<\/th>\norigin.type<\/th>\njarvis.bandgap.mbj<\/th>\nmaterial.elements.2<\/th>\noqmd.magnetic_moment.value<\/th>\n<\/tr>\n<\/thead>\n
0<\/th>\n2<\/td>\n221<\/td>\n25.2675<\/td>\nTrue<\/td>\n520.0<\/td>\nPBE<\/td>\nASCII text, with very long lines, with no line...<\/td>\n.json<\/td>\nglobus:\/\/e38ee745-6d04-11e5-ba46-22000b92c6ec\/...<\/td>\n10833<\/td>\n...<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
1<\/th>\n2<\/td>\n221<\/td>\n24.0794<\/td>\nTrue<\/td>\n520.0<\/td>\nPBE<\/td>\nASCII text, with very long lines, with no line...<\/td>\n86132.json<\/td>\nglobus:\/\/e38ee745-6d04-11e5-ba46-22000b92c6ec\/...<\/td>\n11014<\/td>\n...<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
2<\/th>\n4<\/td>\n225<\/td>\n40.8748<\/td>\nTrue<\/td>\n520.0<\/td>\nPBE<\/td>\nASCII text, with very long lines, with no line...<\/td>\n.json<\/td>\nglobus:\/\/e38ee745-6d04-11e5-ba46-22000b92c6ec\/...<\/td>\n11526<\/td>\n...<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
3<\/th>\n6<\/td>\n227<\/td>\n62.5236<\/td>\nTrue<\/td>\n520.0<\/td>\nPBE<\/td>\nASCII text, with very long lines, with no line...<\/td>\n19313.json<\/td>\nglobus:\/\/e38ee745-6d04-11e5-ba46-22000b92c6ec\/...<\/td>\n11320<\/td>\n...<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n
4<\/th>\n4<\/td>\n139<\/td>\n40.6980<\/td>\nTrue<\/td>\n520.0<\/td>\nPBE<\/td>\nASCII text, with very long lines, with no line...<\/td>\n71045.json<\/td>\nglobus:\/\/e38ee745-6d04-11e5-ba46-22000b92c6ec\/...<\/td>\n11283<\/td>\n...<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\nNaN<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
5 rows \u00d7 48 columns<\/p>\n
 <\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u8bfb\u53d6mat\u6570\u636e_matlab\u5b58\u50a8\u6570\u636e\u4e3amatmatminer\u83b7\u53d6\u6570\u636e_matminer\u83b7\u53d6\u6570\u636e","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"_links":{"self":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts\/5911"}],"collection":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/comments?post=5911"}],"version-history":[{"count":0,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/posts\/5911\/revisions"}],"wp:attachment":[{"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/media?parent=5911"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/categories?post=5911"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mushiming.com\/wp-json\/wp\/v2\/tags?post=5911"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}