journal article·2025-04

A machine learning-based framework for predicting the power factor of thermoelectric materials

Yuxuan Zeng, Wei Cao, Tan Peng, Yue Hou, Ling Miao, Ziyu Wang, Jing Shi

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OpenAlex topics: Machine Learning in Materials Science · Advanced Thermoelectric Materials and Devices · Thermography and Photoacoustic Techniques

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References (97)

Show source-provided references
1. Thermoelectric properties of strained β-Cu2Se10.1021/acsami.1c08686 ↗
2. The effect of doping on thermoelectric performance of p-type snse: Promising thermoelectric material10.1016/j.jallcom.2016.01.190 ↗
3. High performance p-type PbTe-based thermoelectric materials10.1021/ja1010948 ↗
4. Half-Heusler thermoelectrics: Advances from materials fundamental to device engineering
5. Layered thermoelectric materials: Structure, bonding, and performance mechanisms10.1063/5.0074489 ↗
6. Bi2Te3-based applied thermoelectric materials: research advances and new challenges10.1093/nsr/nwaa259 ↗
7. Cu2ZnSnS4: A potential thermoelectric material with promising electronic transport10.1103/PhysRevApplied.22.044075 ↗
8. Highly stabilized and efficient thermoelectric copper selenide10.1038/s41563-024-01815-1 ↗
9. Machine learning accelerates the materials discovery
10. The central role of density functional theory in the ai age10.1126/science.abn3445 ↗
11. Automated search for new thermoelectric materials: the case of liznsb10.1021/ja062526a ↗
12. Machine learning for molecular and materials science10.1038/s41586-018-0337-2 ↗
13. Machine learning: trends, perspectives, and prospects10.1126/science.aaa8415 ↗
14. Machine learning in materials informatics: recent applications and prospects10.1038/s41524-017-0056-5 ↗
15. A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis
16. Machine learning for perovskite materials design and discovery10.1038/s41524-021-00495-8 ↗
17. Commentary: The materials project: A materials genome approach to accelerating materials innovation10.1063/1.4812323 ↗
18. The open quantum materials database (oqmd): assessing the accuracy of dft formation energies10.1038/npjcompumats.2015.10 ↗
19. Matminer: An open source toolkit for materials data mining10.1016/j.commatsci.2018.05.018 ↗
20. Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties10.1063/1.4952607 ↗
21. Data mining our way to the next generation of thermoelectrics10.1016/j.scriptamat.2015.04.026 ↗
22. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach10.1002/jcc.25067 ↗
23. A statistical approach for the rapid prediction of electron relaxation time using elemental representatives10.1021/acs.chemmater.0c01778 ↗
24. Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials10.1039/D0TC02855J ↗
25. AI-driven ensemble learning for accurate seebeck coefficient prediction in half-Heusler compounds based on chemical formulas
26. A unified approach to interpreting model predictions
27. Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, why should i trust you? explaining the predictions of any classifier, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144.10.1145/2939672.2939778 ↗
28. A review of ensemble learning based feature selection10.1080/02564602.2014.906859 ↗
29. Sisso: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
30. An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (k0. 5na0. 5) nbo3-based ceramics10.1038/s41524-023-01187-1 ↗
31. Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence10.1038/s41524-023-01063-y ↗
32. Active learning for the power factor prediction in diamond-like thermoelectric materials10.1038/s41524-020-00439-8 ↗
33. First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning10.1038/s41524-021-00562-0 ↗
34. Predicting thermoelectric transport properties from composition with attention-based deep learning
35. Machine learning in materials science10.1002/inf2.12028 ↗
36. Data from: An ab initio electronic transport database for inorganic materials
37. Georg KH madsen and david j singh
38. Floating-point arithmetic10.1017/S0962492922000101 ↗
39. Accelerated design of fe-based soft magnetic materials using machine learning and stochastic optimization10.1016/j.actamat.2020.05.006 ↗
40. Interpretable machine learning-assisted design of fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity10.1016/j.jmst.2023.12.009 ↗
41. Fast and accurate modeling of molecular atomization energies with machine learning10.1103/PhysRevLett.108.058301 ↗
42. Crystal structure representations for machine learning models of formation energies10.1002/qua.24917 ↗
43. Unified representation of molecules and crystals for machine learning
44. Dscribe: Library of descriptors for machine learning in materials science10.1016/j.cpc.2019.106949 ↗
45. A general-purpose machine learning framework for predicting properties of inorganic materials10.1038/npjcompumats.2016.28 ↗
46. Ridge regression: Biased estimation for nonorthogonal problems10.1080/00401706.1970.10488634 ↗
47. Tianqi Chen, Carlos Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794.10.1145/2939672.2939785 ↗
48. Lightgbm: A highly efficient gradient boosting decision tree
49. Catboost: unbiased boosting with categorical features
50. Edward Rasmussen · 2003 · 63
51. Jun Hua Zhao, Zhao Yang Dong, Zhao Xu, Effective feature preprocessing for time series forecasting, in: International Conference on Advanced Data Mining and Applications, Springer, pp. 769–781.10.1007/11811305_84 ↗
52. Various dimension reduction techniques for high dimensional data analysis: a review10.1007/s10462-020-09928-0 ↗
53. Overview and comparative study of dimensionality reduction techniques for high dimensional data10.1016/j.inffus.2020.01.005 ↗
54. Feature wise normalization: An effective way of normalizing data10.1016/j.patcog.2021.108307 ↗
55. A comparative analysis of gradient boosting algorithms10.1007/s10462-020-09896-5 ↗
56. The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation10.7717/peerj-cs.623 ↗
57. The predictive sample reuse method with applications10.1080/01621459.1975.10479865 ↗
58. Multiple predicting k-fold cross-validation for model selection10.1080/10485252.2017.1404598 ↗
59. From ultrasoft pseudopotentials to the projector augmented-wave method10.1103/PhysRevB.59.1758 ↗
60. Generalized gradient approximation made simple10.1103/PhysRevLett.77.3865 ↗
61. Special points for brillouin-zone integrations10.1103/PhysRevB.13.5188 ↗
62. Boltztrap2, a program for interpolating band structures and calculating semi-classical transport coefficients.10.1016/j.cpc.2018.05.010 ↗
63. Efficient calculation of carrier scattering rates from first principles10.1038/s41467-021-22440-5 ↗
64. Machine learning problems from optimization perspective10.1007/s10898-008-9364-0 ↗
65. Why do tree-based models still outperform deep learning on typical tabular data?
66. Understanding the limitations of deep models for molecular property prediction: Insights and solutions
67. No free lunch theorems for optimization10.1109/4235.585893 ↗
68. The space groups of icosahedral quasicrystals and cubic
69. Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R Chandrasekhar, Predicting thermoelectric properties from crystal graphs and material descriptors-first application for functional materials.
70. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set10.1103/PhysRevB.54.11169 ↗
71. Optimized pseudopotentials10.1103/PhysRevB.41.1227 ↗
72. Crystal structure induced ultralow lattice thermal conductivity in thermoelectric ag9alse610.1002/aenm.201800030 ↗
73. Advanced thermoelectric design: from materials and structures to devices10.1021/acs.chemrev.0c00026 ↗
74. Rational design of advanced thermoelectric materials10.1002/aenm.201200514 ↗
75. Nando Kaminski, State of the art and the future of wide band-gap devices, in: 2009 13th European Conference on Power Electronics and Applications, pp. 1–9, IEEE.
76. Dimensionality effects in high-performance thermoelectric materials: Computational and experimental progress in energy harvesting applications
77. Correlation of some opto-electrical properties of se–te–sn glassy semiconductors with the average single bond energy and the average electronegativity10.1016/j.jallcom.2015.11.161 ↗
78. Effect of tellurium addition on the optical and physical properties of germanium selenide glassy semiconductors10.1016/j.vacuum.2009.10.046 ↗
79. Electrical transport mechanisms in a-se95m5 films (m+ga, sb, bi)10.1016/j.materresbull.2010.02.011 ↗
80. High carrier mobility and high figure of merit in the cubise2 alloyed gete10.1002/aenm.202102913 ↗
81. The role of electronegativity in the thermoelectric performance of gete–i–v–vi 2 solid solutions10.1039/D0TA10268G ↗
82. A scale of electronegativity based on electrostatic force10.1016/0022-1902(58)80003-2 ↗
83. The periodic electronegativity table10.1515/znb-2008-0214 ↗
84. Crystal instability and melting10.1038/299188b0 ↗
85. Investigation on low-temperature thermoelectric properties of ag2se polycrystal fabricated by using zone-melting method10.1021/acs.jpclett.1c02139 ↗
86. Molecular-dynamics studies of crystal defects and melting10.1103/PhysRevB.39.9302 ↗
87. Effect of crystal defects on the melting temperature of ni and al
88. Revealing the defect-dominated electron scattering in mg3sb2-based thermoelectric materials10.34133/2022/9875329 ↗
89. Charge carrier scattering by defects in semiconductors10.1103/PhysRevB.81.235204 ↗
90. Re-doped p-type thermoelectric SnSe polycrystals with enhanced power factor and high zt > 210.1002/adfm.202301971 ↗
91. Realizing high-performance thermoelectric module through enhancing power factor via optimizing carrier mobility in n-type PbSe crystals
92. Regulating te vacancies through dopant balancing via excess ag enables rebounding power factor and high thermoelectric performance in p-type pbte
93. Modulation doping enables ultrahigh power factor and thermoelectric zt in n-type bi2te2. 7se0. 310.1002/advs.202201353 ↗
94. Convergence of multi-valley bands as the electronic origin of high thermoelectric performance in cosb3 skutterudites10.1038/nmat4430 ↗
95. Xgboost model for electrocaloric temperature change prediction in ceramics10.1038/s41524-022-00826-3 ↗
96. Optimizing neural networks for enhanced material property predictions: Insights from bulk modulus analysis
97. Machine learning based prediction of space group for ba(ce0.8−xzrx)y0.2o3 perovskite-type protonic conductors10.1016/j.ceramint.2022.10.019 ↗