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dataset_mg_alloy

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Mg(wt.%)
float64
75.2
99.3
Zn(wt.%)
float64
0
15.2
Y(wt.%)
float64
0
18.1
Zr(wt.%)
float64
0
2
Nd(wt.%)
float64
0
4
Gd(wt.%)
float64
0
20
solution temperature(°Ê)
int64
0
550
solution time(h)
float64
0
25
homogenization temperature(°Ê)
int64
0
560
homogenization time(h)
int64
0
48
extrusion temperature(°Ê)
int64
0
500
extrusion ratio
float64
0
42
aging temperature(°Ê)
int64
0
250
aging time(h)
float64
0
126
UTS(MPa)
float64
75
483
YS(MPa)
float64
20.9
425
EL(%)
float64
0.5
37.6
99.27
0
0.73
0
0
0
525
7
0
0
0
0
0
0
150
39
14
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1.23
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7
0
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155
45
13
96.92
0
3.08
0
0
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7
0
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0
160
70
11
95.33
0
4.67
0
0
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7
0
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89
10
93.45
0
6.55
0
0
0
525
7
0
0
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230
110
8
98.99
0
1.01
0
0
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0
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0
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0
75
25
10
97
0
3
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8
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0
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120
33
98
0
2
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0
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0
0
228
146
30.5
98.13
0
0
0
0
1.87
0
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0
0
0
0
0
103.73
37.992
6.362
98.13
0
0
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0
1.87
525
24
0
0
0
0
0
0
87.002
33.43
4.928
98.13
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0
0
0
1.87
525
24
0
0
0
0
250
6
101.368
41.274
5.686
95.33
0
0
0
0
4.67
0
0
0
0
0
0
0
0
128.468
54.752
6.62
95.33
0
0
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0
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525
24
0
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0
0
0
98.012
44.85
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95.33
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0
4.67
525
24
0
0
0
0
250
6
78.658
42.604
4.27
90.8
0
0
0
0
9.2
0
0
0
0
0
0
0
0
131.152
84.11
2.5
90.8
0
0
0
0
9.2
525
24
0
0
0
0
0
0
111.65
69.12
3.152
90.8
0
0
0
0
9.2
525
24
0
0
0
0
250
6
132.258
85.43
2.182
85.95
0
0
0
0
14.05
0
0
0
0
0
0
0
0
175.22
127.646
0.95
85.95
0
0
0
0
14.05
525
24
0
0
0
0
0
0
186.844
118.052
2.44
85.95
0
0
0
0
14.05
525
24
0
0
0
0
250
6
250.918
201.396
0.74
96.89
0
0
0
0
3.11
535
1.5
0
0
0
0
0
0
160
60
13
94.27
0
0
0
0
5.73
535
4
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0
0
0
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0
180
80
11
90.72
0
0
0
0
9.28
535
6.5
0
0
0
0
0
0
190
100
9
85.8
0
0
0
0
14.2
535
9
0
0
0
0
0
0
225
130
8
80.4
0
0
0
0
19.6
540
9.5
0
0
0
0
0
0
255
150
7.5
80
0
0
0
0
20
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0
0
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0
254
254
13
98.45
0
0
0
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1.55
550
8
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450
30
0
0
214
102
23.9
98.45
0
0
0
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1.55
550
8
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30
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210
130
15.8
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0
0
1.2
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0
0
0
0
123
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4.61
98.15
0
0
0
1.85
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155.8
121.2
2.76
96.41
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0
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153.6
141.2
1.08
98
0
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380
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77
30
97.6
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310
10
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0
220
125
26
97.6
2
0.4
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310
10
0
0
240
160
30
97.6
2
0
0
0.4
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0
310
10
0
0
245
175
28
82.3
14.4
3.3
0
0
0
480
24
0
0
430
25
0
0
320
171
12
82.3
14.4
3.3
0
0
0
480
24
0
0
300
25
150
30
380
365
8
99.1
0
0.4
0.5
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0
0
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0
0
0
0
119.3
20.9
4.3
99.1
0
0
0.5
0
0.4
0
0
0
0
0
0
0
0
144.6
52.2
22.1
91.4
0
0
0.6
0
8
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0
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0
0
141
82
6.2
91.4
0
0
0.6
0
8
530
10
0
0
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0
230
90
155
81
6.4
86.6
0
3
0.4
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10
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0
0
0
230
151
4.4
86.6
0
3
0.4
0
10
500
6
0
0
0
0
0
0
247
131
14.4
86.6
0
3
0.4
0
10
500
6
0
0
0
0
225
30
349
231
2.2
93
0.2
4
0.4
2.4
0
0
0
0
0
0
0
0
0
197
150
7.5
93
0.2
4
0.4
2.4
0
490
20
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0
0
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240
162
15
93
0.2
4
0.4
2.4
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490
20
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200
70
339
268
4
93
0.2
4
0.4
2.4
0
500
6
0
0
0
0
225
20
330
265
6.5
93
0.2
4
0.4
2.4
0
510
4
0
0
0
0
250
5
260
195
3
96.7
0.2
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260
29
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1.6
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90
12
97.21
0.11
0
0.43
2.25
0
540
10
0
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8
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204
20.6
96.69
0.2
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540
10
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350
8
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245
163
14
96.1
0.4
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520
8
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200
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293
163
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98.7
0
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140.2
51.7
27.7
91.5
0
0
0.5
2
6
500
6
0
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0
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0
220
118
17
91.5
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0.5
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500
6
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200
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345
175
7.5
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0
0
0.5
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6
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0
500
6
450
9.3
0
0
275
200
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91.5
0
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500
6
350
9.3
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250
8
91.5
0
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0.5
2
6
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0
500
6
450
9.3
200
24
290
245
29
91.5
0
0
0.5
2
6
0
0
500
6
350
9.3
200
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375
275
17.5
86.5
0
3
0.5
0
10
0
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187
178
3.2
84.6
0
3
0.4
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12
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400
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282.5
187.3
6.3
86.6
1
3.8
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8.2
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187
119
2.1
86.6
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3.8
0.4
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8.2
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12
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5.5
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440
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350
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300
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335.5
236.1
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189
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104
4.7
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Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning

Dataset containing mechanical properties of 365 Mg alloys

Dataset Information

  • Source: Foundry-ML
  • DOI: 10.18126/myj4-0h48
  • Year: 2023
  • Authors: Hou, Haobing, Wang, Jianfeng, Ye, Li, Zhu, Shijie, Wang, Liguo, Guan, Shaokang
  • Data Type: tabular

Fields

Field Role Description Units
Mg(wt.%) input Amount of Mg wt%
Zn(wt.%) input Amount of Zn wt%
Y(wt.%) input Amount of Y wt%
Zr(wt.%) input Amount of Zr wt%
Nd(wt.%) input Amount of Nd wt%
Gd(wt.%) input Amount of Gd wt%
solution temperature(°Ê) input Solution temperature degC
solution time(h) input Solution time hours
homogenization temperature(°Ê) input Homogenization temperature degC
homogenization time(h) input Homogenization time hours
extrusion temperature(°Ê) input Extrusion temperature degC
extrusion ratio input Extrusion ratio
aging temperature(°Ê) input Aging temperature degC
aging time(h) input Aging time hours
UTS(MPa) target Ultimate tensile strength MPa
YS(MPa) target Yield strength MPa
EL(%) target Elongation

Splits

  • train: train

Usage

With Foundry-ML (recommended for materials science workflows) 

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/myj4-0h48")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets 

from datasets import load_dataset

dataset = load_dataset("Dataset_Mg_alloy")

Citation 

@misc{https://doi.org/10.18126/myj4-0h48
doi = {10.18126/myj4-0h48}
url = {https://doi.org/10.18126/myj4-0h48}
author = {Hou, Haobing and Wang, Jianfeng and Ye, Li and Zhu, Shijie and Wang, Liguo and Guan, Shaokang}
title = {Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2023}}

License

other

This dataset was exported from Foundry-ML, a platform for materials science datasets.

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