Journal of Inorganic Materials ›› 2022, Vol. 37 ›› Issue (12): 1311-1320.DOI: 10.15541/jim20220149
• RESEARCH ARTICLE • Previous Articles Next Articles
SHI Siqi1,2,5(), SUN Shiyu1, MA Shuchang3, ZOU Xinxin3, QIAN Quan3,4,5, LIU Yue3,4,5(
)
Received:
2022-03-21
Revised:
2022-05-06
Published:
2022-12-20
Online:
2022-05-27
Contact:
LIU Yue, professor. E-mail: yueliu@shu.edu.cnAbout author:
SHI Siqi (1978-), male, PhD, professor. E-mail: sqshi@shu.edu.cn
Supported by:
CLC Number:
SHI Siqi, SUN Shiyu, MA Shuchang, ZOU Xinxin, QIAN Quan, LIU Yue. Detection Method on Data Accuracy Incorporating Materials Domain Knowledge[J]. Journal of Inorganic Materials, 2022, 37(12): 1311-1320.
Data-driven data accuracy detection | DADMmdk | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Stage | N | Number of anomalous | Number of removed | N1 | Number of anomalous | Number of removed | Number of corrected | N1 | |||||
D | S | D | S | D | S | D | S | D | S | ||||
1 | 90 | 18 | 0 | 0 | 5 | 85 | 18 | 2 | 0 | 5 | 0 | 0 | 85 |
2 | 85 | 0 | 0 | 0 | 0 | 85 | 0 | 0 | 0 | 0 | 0 | 0 | 85 |
3 | 85 | 0 | 9 | 0 | 0 | 85 | 0 | 9 | 0 | 0 | 0 | 3 | 85 |
Table 1 Result comparison of data-driven data accuracy detection methods with DADMmdk
Data-driven data accuracy detection | DADMmdk | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Stage | N | Number of anomalous | Number of removed | N1 | Number of anomalous | Number of removed | Number of corrected | N1 | |||||
D | S | D | S | D | S | D | S | D | S | ||||
1 | 90 | 18 | 0 | 0 | 5 | 85 | 18 | 2 | 0 | 5 | 0 | 0 | 85 |
2 | 85 | 0 | 0 | 0 | 0 | 85 | 0 | 0 | 0 | 0 | 0 | 0 | 85 |
3 | 85 | 0 | 9 | 0 | 0 | 85 | 0 | 9 | 0 | 0 | 0 | 3 | 85 |
No. | ICSD | Formula | Valence_M1 | |
---|---|---|---|---|
17 | 182793 | Na16.74Cr12P18O72 | 3.105 | |
72 | 71326 | Na3Nb12P18O72 | 4.25 |
Table 2 Anomalous points in single-dimensional data correctness detection
No. | ICSD | Formula | Valence_M1 | |
---|---|---|---|---|
17 | 182793 | Na16.74Cr12P18O72 | 3.105 | |
72 | 71326 | Na3Nb12P18O72 | 4.25 |
Fig. 3 Anomalous points detection results (a) All anomalous points from the box plot; Anomalous points in (b) Radius_X1, (c) Occu_X1, (d) Occu_X2, (e) EaColorful figures are available on website
Fig. 4 Result of PCC The color bands represents the mapping of values to colors; The darker the color (red or cyan), the higher the correlation, and vice versa.
Fig. 5 Clustering results of feature and activation energy (a-h) Clustering overlaps of features and activation energy for each of 8 clusters; Horizontal and vertical coordinates represent two dimensions after dimension reduction by t-SNE (t-distributed Stochastic Neighbor Embedding)Colorful figures are available on website
No. | ICSD | Formula | a | c | Vcell | Revised a | Revised c | Revised Vcell |
---|---|---|---|---|---|---|---|---|
5 | 15545 | Na24Zr12Si18O72 | 9.186 | 22.181 | 1621.04 | 9.198 | 22.210 | 1627.29 |
6 | 15546 | Na24Zr12Si18O72 | 9.186 | 22.181 | 1621.04 | 9.199 | 22.470 | 1646.70 |
7 | 15547 | Na24Zr12Si18O72 | 9.186 | 22.181 | 1621.04 | 9.199 | 22.706 | 1663.99 |
Table 3 Anomalous data detection and correction
No. | ICSD | Formula | a | c | Vcell | Revised a | Revised c | Revised Vcell |
---|---|---|---|---|---|---|---|---|
5 | 15545 | Na24Zr12Si18O72 | 9.186 | 22.181 | 1621.04 | 9.198 | 22.210 | 1627.29 |
6 | 15546 | Na24Zr12Si18O72 | 9.186 | 22.181 | 1621.04 | 9.199 | 22.470 | 1646.70 |
7 | 15547 | Na24Zr12Si18O72 | 9.186 | 22.181 | 1621.04 | 9.199 | 22.706 | 1663.99 |
Model | Origin data | Revised data | ||||
---|---|---|---|---|---|---|
RMSE | MAPE | R2 | RMSE | MAPE | R2 | |
LASSO | 0.621 | 0.218 | 0.118 | 0.058 | 0.035 | 0.943 |
GPR | 0.637 | 0.231 | 0.073 | 0.051 | 0.032 | 0.957 |
Ridge | 0.548 | 0.244 | 0.313 | 0.051 | 0.033 | 0.956 |
SVR | 0.638 | 0.102 | 0.072 | 0.071 | 0.057 | 0.916 |
KNN | 0.624 | 0.226 | 0.108 | 0.079 | 0.051 | 0.894 |
RF | 0.400 | 0.088 | 0.629 | 0.051 | 0.035 | 0.956 |
Table 4 Experimental results of ML models
Model | Origin data | Revised data | ||||
---|---|---|---|---|---|---|
RMSE | MAPE | R2 | RMSE | MAPE | R2 | |
LASSO | 0.621 | 0.218 | 0.118 | 0.058 | 0.035 | 0.943 |
GPR | 0.637 | 0.231 | 0.073 | 0.051 | 0.032 | 0.957 |
Ridge | 0.548 | 0.244 | 0.313 | 0.051 | 0.033 | 0.956 |
SVR | 0.638 | 0.102 | 0.072 | 0.071 | 0.057 | 0.916 |
KNN | 0.624 | 0.226 | 0.108 | 0.079 | 0.051 | 0.894 |
RF | 0.400 | 0.088 | 0.629 | 0.051 | 0.035 | 0.956 |
No. | Descriptors | Description | Type | Range | Source |
---|---|---|---|---|---|
1 | Occu_6b | Occupancy of Na in 6b site | float | [0,1] | CIF |
2 | Occu_18e | Occupancy of Na in 18e site | float | [0,1] | CIF |
3 | Occu_36f | Occupancy of Na in 36f site | float | [0,1] | CIF |
4 | C_Na | Na+ concentration | float | (0,+∞) | Formula |
5 | Occu_M1 | Occupancy of element M1 | float | [0,1] | CIF |
6 | Occu_M2 | Occupancy of element M2 | float | [0,1] | CIF |
7 | EN_M1 | Electronegativity of element M1 | float | (0,+∞) | Pauling electronegativity meter |
8 | EN_M2 | Electronegativity of element M2 | float | [0,+∞) | Pauling electronegativity meter |
9 | EN_avg_M | Average effective electronegativity of M site | float | (0,+∞) | Formula |
10 | Radius_M1 | Ionic radius of element M1 | float | (0,+∞) | Shannon radius table |
11 | Radius_M2 | Ionic radius of element M2 | float | [0,+∞) | Shannon radius table |
12 | Radius_avg_M | Average effective ionic radius of M site | float | (0,+∞) | Formula |
13 | Valence_M1 | Valence of element M1 | int | (0,+∞) | CIF |
14 | Valence_M2 | Valence of element M2 | int | [0,+∞) | CIF |
15 | Valence_avg_M | Average effective ionic valence of M site | float | (0,+∞) | Formula |
16 | Occu_X1 | Occupancy of element X1 | float | [0,1] | CIF |
17 | Occu_X2 | Occupancy of element X2 | float | [0,1] | CIF |
18 | EN_X1 | Electronegativity of element X1 | float | (0,+∞) | CIF |
19 | EN_X2 | Electronegativity of element X2 | float | [0,+∞) | CIF |
20 | EN_avg_X | Average effective electronegativity of X site | float | (0,+∞) | Formula |
21 | Radius_X1 | Ionic radius of element X1 | float | (0,+∞) | Shannon radius table |
22 | Radius_X2 | Ionic radius of element X2 | float | [0,+∞) | Shannon radius table |
23 | Radius_avg_X | Average effective ionic radius of X site | float | (0,+∞) | Formula |
24 | Valence_X1 | Valence of element X1 | int | (0,+∞) | CIF file |
25 | Valence_X2 | Valence of element X2 | int | [0,+∞) | CIF file |
26 | Valence_avg_X | Average effective ionic valence of X site | float | (0,+∞) | Formula |
27 | a | Lattice parameter | float | (0,+∞) | CIF file |
28 | c | Lattice parameter | float | (0,+∞) | CIF file |
29 | Vcell | Lattice parameter | float | (0,+∞) | Formula |
30 | V_MO6 | Volume of MO6 polyhedron | float | (0,+∞) | VESTA file |
31 | V_XO4 | Volume of XO4 polyhedron | float | (0,+∞) | VESTA file |
32 | V_Na1O6 | Volume of Na1O6 polyhedron | float | (0,+∞) | VESTA file |
33 | V_Na2O8 | Volume of Na2O8 polyhedron | float | (0,+∞) | VESTA file |
34 | V_Na3O5 | Volume of Na3O5 polyhedron | float | (0,+∞) | VESTA file |
35 | BT1 | Bottleneck | float | (0,+∞) | Formula |
36 | BT2 | Bottleneck | float | (0,+∞) | Formula |
37 | min_BT | The minimum of BT2 and BT1 | float | (0,+∞) | VESTA file |
38 | RT | Radius of largest sphere probe that can freely pass through the void space packed by framework ions | float | (0,+∞) | Geometry-based Ion-transport Analysis Library CAVD |
39 | EP_6b | Configurational entropy of Na in 6b site | float | [0,+∞) | Formula |
40 | EP_18e | Configurational entropy of Na in 18e site | float | [0,+∞) | Formula |
41 | EP_36f | Configurational entropy of Na in 36f site | float | [0,+∞) | Formula |
42 | EP_Na | Configurational entropy of Na | float | [0,+∞) | Formula |
43 | EP_M | Configurational entropy of cationic in M site | float | [0,+∞) | Formula |
44 | EP_X | Configurational entropy of cationic in X site | float | [0,+∞) | Formula |
45 | T′ | Temperature | float | (0,+∞) | Reference |
Table S1 The meanings, types, ranges, and sources of all descriptors in NASICON
No. | Descriptors | Description | Type | Range | Source |
---|---|---|---|---|---|
1 | Occu_6b | Occupancy of Na in 6b site | float | [0,1] | CIF |
2 | Occu_18e | Occupancy of Na in 18e site | float | [0,1] | CIF |
3 | Occu_36f | Occupancy of Na in 36f site | float | [0,1] | CIF |
4 | C_Na | Na+ concentration | float | (0,+∞) | Formula |
5 | Occu_M1 | Occupancy of element M1 | float | [0,1] | CIF |
6 | Occu_M2 | Occupancy of element M2 | float | [0,1] | CIF |
7 | EN_M1 | Electronegativity of element M1 | float | (0,+∞) | Pauling electronegativity meter |
8 | EN_M2 | Electronegativity of element M2 | float | [0,+∞) | Pauling electronegativity meter |
9 | EN_avg_M | Average effective electronegativity of M site | float | (0,+∞) | Formula |
10 | Radius_M1 | Ionic radius of element M1 | float | (0,+∞) | Shannon radius table |
11 | Radius_M2 | Ionic radius of element M2 | float | [0,+∞) | Shannon radius table |
12 | Radius_avg_M | Average effective ionic radius of M site | float | (0,+∞) | Formula |
13 | Valence_M1 | Valence of element M1 | int | (0,+∞) | CIF |
14 | Valence_M2 | Valence of element M2 | int | [0,+∞) | CIF |
15 | Valence_avg_M | Average effective ionic valence of M site | float | (0,+∞) | Formula |
16 | Occu_X1 | Occupancy of element X1 | float | [0,1] | CIF |
17 | Occu_X2 | Occupancy of element X2 | float | [0,1] | CIF |
18 | EN_X1 | Electronegativity of element X1 | float | (0,+∞) | CIF |
19 | EN_X2 | Electronegativity of element X2 | float | [0,+∞) | CIF |
20 | EN_avg_X | Average effective electronegativity of X site | float | (0,+∞) | Formula |
21 | Radius_X1 | Ionic radius of element X1 | float | (0,+∞) | Shannon radius table |
22 | Radius_X2 | Ionic radius of element X2 | float | [0,+∞) | Shannon radius table |
23 | Radius_avg_X | Average effective ionic radius of X site | float | (0,+∞) | Formula |
24 | Valence_X1 | Valence of element X1 | int | (0,+∞) | CIF file |
25 | Valence_X2 | Valence of element X2 | int | [0,+∞) | CIF file |
26 | Valence_avg_X | Average effective ionic valence of X site | float | (0,+∞) | Formula |
27 | a | Lattice parameter | float | (0,+∞) | CIF file |
28 | c | Lattice parameter | float | (0,+∞) | CIF file |
29 | Vcell | Lattice parameter | float | (0,+∞) | Formula |
30 | V_MO6 | Volume of MO6 polyhedron | float | (0,+∞) | VESTA file |
31 | V_XO4 | Volume of XO4 polyhedron | float | (0,+∞) | VESTA file |
32 | V_Na1O6 | Volume of Na1O6 polyhedron | float | (0,+∞) | VESTA file |
33 | V_Na2O8 | Volume of Na2O8 polyhedron | float | (0,+∞) | VESTA file |
34 | V_Na3O5 | Volume of Na3O5 polyhedron | float | (0,+∞) | VESTA file |
35 | BT1 | Bottleneck | float | (0,+∞) | Formula |
36 | BT2 | Bottleneck | float | (0,+∞) | Formula |
37 | min_BT | The minimum of BT2 and BT1 | float | (0,+∞) | VESTA file |
38 | RT | Radius of largest sphere probe that can freely pass through the void space packed by framework ions | float | (0,+∞) | Geometry-based Ion-transport Analysis Library CAVD |
39 | EP_6b | Configurational entropy of Na in 6b site | float | [0,+∞) | Formula |
40 | EP_18e | Configurational entropy of Na in 18e site | float | [0,+∞) | Formula |
41 | EP_36f | Configurational entropy of Na in 36f site | float | [0,+∞) | Formula |
42 | EP_Na | Configurational entropy of Na | float | [0,+∞) | Formula |
43 | EP_M | Configurational entropy of cationic in M site | float | [0,+∞) | Formula |
44 | EP_X | Configurational entropy of cationic in X site | float | [0,+∞) | Formula |
45 | T′ | Temperature | float | (0,+∞) | Reference |
Input | Original dataset |
---|---|
Output | Revised dataset |
# Single-dimensional data correctness detection | |
1 | For |
2 | For |
3 | If |
4 | |
5 | If |
6 | According to the material domain knowledge, the anomaly points in |
7 | |
8 | For |
9 | |
10 | According to the material domain knowledge, the anomaly points in |
# Multi-dimensional data correlation detection | |
11 | For |
12 | |
13 | If |
14 | |
15 | According to the material domain knowledge, the anomaly points in |
# Full-dimensional data reliability detection | |
16 | |
17 | For |
18 | If |
19 | |
20 | If |
21 | According to the material domain knowledge, the anomaly points in |
22 | |
23 | For |
24 | |
25 | According to the material domain knowledge, the anomaly points in |
Algorithm S1 Data accuracy detection algorithm incorporating descriptor knowledge
Input | Original dataset |
---|---|
Output | Revised dataset |
# Single-dimensional data correctness detection | |
1 | For |
2 | For |
3 | If |
4 | |
5 | If |
6 | According to the material domain knowledge, the anomaly points in |
7 | |
8 | For |
9 | |
10 | According to the material domain knowledge, the anomaly points in |
# Multi-dimensional data correlation detection | |
11 | For |
12 | |
13 | If |
14 | |
15 | According to the material domain knowledge, the anomaly points in |
# Full-dimensional data reliability detection | |
16 | |
17 | For |
18 | If |
19 | |
20 | If |
21 | According to the material domain knowledge, the anomaly points in |
22 | |
23 | For |
24 | |
25 | According to the material domain knowledge, the anomaly points in |
No. | Descriptor | Description |
---|---|---|
1 | Occu_6b | Occupancy of Na in 6b site |
2 | Occu_36f | Occupancy of Na in 36f site |
3 | Occu_M2 | Occupancy of element M2 |
4 | Occu_X1 | Occupancy of element X1 |
5 | Occu_X2 | Occupancy of element X2 |
6 | EN_M1 | Electronegativity of element M1 |
7 | EN_avg_M | Average effective electronegativity of M site |
8 | EN_avg_X | Average effective electronegativity of X site |
9 | Radius_X1 | Ionic radius of element X1 |
10 | Radius_avg_X | Average effective ionic radius of X site |
11 | Valence_avg_X | Average effective ionic valence of X site |
12 | V_XO4 | Volume of XO4 polyhedron |
13 | V_Na3O5 | Volume of Na3O5 polyhedron |
14 | min_BT | The minimum of BT2 and BT1 |
15 | EP_36f | Configurational entropy of Na in 36f site |
16 | EP_X | Configurational entropy of Na in X site |
17 | T | Temperature |
18 | Ea | Activation energy |
Table S2 Anomalous data detection and correction
No. | Descriptor | Description |
---|---|---|
1 | Occu_6b | Occupancy of Na in 6b site |
2 | Occu_36f | Occupancy of Na in 36f site |
3 | Occu_M2 | Occupancy of element M2 |
4 | Occu_X1 | Occupancy of element X1 |
5 | Occu_X2 | Occupancy of element X2 |
6 | EN_M1 | Electronegativity of element M1 |
7 | EN_avg_M | Average effective electronegativity of M site |
8 | EN_avg_X | Average effective electronegativity of X site |
9 | Radius_X1 | Ionic radius of element X1 |
10 | Radius_avg_X | Average effective ionic radius of X site |
11 | Valence_avg_X | Average effective ionic valence of X site |
12 | V_XO4 | Volume of XO4 polyhedron |
13 | V_Na3O5 | Volume of Na3O5 polyhedron |
14 | min_BT | The minimum of BT2 and BT1 |
15 | EP_36f | Configurational entropy of Na in 36f site |
16 | EP_X | Configurational entropy of Na in X site |
17 | T | Temperature |
18 | Ea | Activation energy |
No. | ICSD | Formula |
---|---|---|
6 | 15546 | Na24Zr12Si18O72 |
29 | 202713 | Na14.94Zr10.8Sc1.2Si7.74P10.26O72 |
30 | 202860 | Na6Mo12P18O72 |
47 | 260210 | Na24Fe12P18O72 |
56 | 35770 | Na4.0002Co3Mo22.332O72 |
59 | 421531 | Na6Ti12As18O72 |
75 | 72218 | Na6Sn12P18O72 |
84 | 97956 | Na6Zr12As18O72 |
89 | 235775 | Na3MnZr(PO4)3 |
Table S3 Anomalous samples based on OCSVM
No. | ICSD | Formula |
---|---|---|
6 | 15546 | Na24Zr12Si18O72 |
29 | 202713 | Na14.94Zr10.8Sc1.2Si7.74P10.26O72 |
30 | 202860 | Na6Mo12P18O72 |
47 | 260210 | Na24Fe12P18O72 |
56 | 35770 | Na4.0002Co3Mo22.332O72 |
59 | 421531 | Na6Ti12As18O72 |
75 | 72218 | Na6Sn12P18O72 |
84 | 97956 | Na6Zr12As18O72 |
89 | 235775 | Na3MnZr(PO4)3 |
Method | Description | Advantage | Disadvantage | Application scope |
---|---|---|---|---|
KNN[ | The nonparametric and distance-based outlier detection method in one-dimensional or multi-dimensional feature space, which depends on the distance measure between data points. | Simple; There is no need to estimate the distribution. | The results are susceptible to the influence of parameters; Not applicable to large data sets. | Small and medium sample data sets. |
LOF[ | An outlier detection method based on "density", which considers the outlier data points different from the surrounding data points in density. | Provide quantitative measurement of outliers. | It is difficult to select parameters; It is not suitable for detecting outliers in the whole region. | Small-scale dataset |
IForest[ | A method considering the division of sample data from each dimension, the earlier the data points are divided into separate areas, the more likely they are outliers. | It has linear time scaling. | Only sensitive to sparse global points, not ideal for dealing with locally sparse points. | Low dimensional data with a small proportion of abnormal data in the total sample size. |
OCSVM[ | A method that the normal samples are divided in the sphere, and the abnormal samples are divided outside the sphere by looking for the hypersphere. | It can be used for high-dimensional data. | The computation cost is higher. | Data distribution has no hypothesis of high dimensional data. |
MCD[ | A method for detecting outliers by location and distribution estimation algorithm. | Simple implementation and robustness. | With the increase of data dimension, the efficiency decreases. | Large-scale high dimensional data. |
Table S4 Comparison of anomaly detection methods for multi-dimensional data
Method | Description | Advantage | Disadvantage | Application scope |
---|---|---|---|---|
KNN[ | The nonparametric and distance-based outlier detection method in one-dimensional or multi-dimensional feature space, which depends on the distance measure between data points. | Simple; There is no need to estimate the distribution. | The results are susceptible to the influence of parameters; Not applicable to large data sets. | Small and medium sample data sets. |
LOF[ | An outlier detection method based on "density", which considers the outlier data points different from the surrounding data points in density. | Provide quantitative measurement of outliers. | It is difficult to select parameters; It is not suitable for detecting outliers in the whole region. | Small-scale dataset |
IForest[ | A method considering the division of sample data from each dimension, the earlier the data points are divided into separate areas, the more likely they are outliers. | It has linear time scaling. | Only sensitive to sparse global points, not ideal for dealing with locally sparse points. | Low dimensional data with a small proportion of abnormal data in the total sample size. |
OCSVM[ | A method that the normal samples are divided in the sphere, and the abnormal samples are divided outside the sphere by looking for the hypersphere. | It can be used for high-dimensional data. | The computation cost is higher. | Data distribution has no hypothesis of high dimensional data. |
MCD[ | A method for detecting outliers by location and distribution estimation algorithm. | Simple implementation and robustness. | With the increase of data dimension, the efficiency decreases. | Large-scale high dimensional data. |
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