Table 4 Prediction performance benchmarking for the prediction task of “Performance against Physical Attributes" on the test set.

From: Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Property

Data size

Base

MAE of best SC model

MAE of best TL model

KLU

28,056

18.77

10.79

11.15

KAA

28,171

5.234

2.722

2.832

BgOptb

28,163

0.988

0.279

0.251

Deltae

28,155

0.850

0.135

0.125

Encut

28,108

246.25

76.99

82.26

Ehull

27,297

0.131

0.058

0.050

Magoszi

25,844

1.225

0.438

0.405

Magout

25,357

1.176

0.393

0.376

Eps

25,150

3.829

1.280

1.262

PPF

16,250

650.5

495.0

494.0

NPF

16,250

658.1

484.2

512.3

Pem300k

16,763

1.918

1.086

1.228

Nem300k

16,760

1.918

1.086

1.197

PSB

14,439

163.30

56.49

60.25

NSB

14,144

108.69

48.93

54.53

Meps

11,349

4.905

1.784

1.776

MaxM

10,963

285.32

57.38

65.69

MinM

10,930

40.89

24.12

23.51

ETC11

10,839

81.66

34.53

33.19

ETC12

10,759

44.96

16.60

17.23

ETC13

10,846

42.54

14.09

13.93

ETC22

10,832

84.06

34.62

31.35

ETC33

10,856

84.12

35.82

34.22

ETC44

9986

29.55

15.23

14.63

ETC55

9755

26.61

12.40

11.74

ETC66

9739

27.59

13.53

13.10

BulkKV

10,743

49.11

11.83

11.28

ShearGV

10,209

24.28

11.26

11.01

BgMbj

7296

1.911

0.555

0.534

Spillage

3866

0.501

0.410

0.373

SLME

3006

9.439

7.193

6.420

MaxIrM

2302

426.0

87.67

103.4

MinIrM

2268

66.16

38.52

44.45

PMDiEl

2126

5.757

3.911

2.715

PMDi

2126

6.977

4.336

3.561

PMDiIo

2126

2.577

0.847

0.774

PMEij

1123

0.520

0.436

0.367

PMDij

689

46.47

26.46

23.46

Exfoli

557

62.93

54.26

51.56

  1. The table shows the test MAE of the best model selected using Supplementary Table 9 (based on validation MAE) when run on the test set for each of the target materials properties. The selected modeling configurations are listed in Supplementary Table 10. The SC models are allowed to use PA as input as well, while TL models only use EF-based inputs. The lowest MAE values in each row are highlighted in bold.
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