Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials
Open accessAbstract
With the rapid development of artificial intelligence and machine learning (ML) methods, materials science is rapidly entering the era of data-driven materials informatics. ML models serve as the most crucial component, closely bridging material structure and material properties. There is a considerable difference in the prediction performance of different ML methods for material systems. Herein, we evaluated three categories (linear, kernel, and nonlinear methods) of models, with twelve ML algorithms commonly used in the materials field. In addition, halide perovskite was chosen as an example to evaluate the fitting performance of different models. We constructed a total dataset of 540 halide perovskites and 72 features, with formation energy and bandgap as target properties. We found that different categories of ML models show similar trends for different target properties. Among them, the difference between the models is enormous for the formation energy, with the coefficient of determination (R2) range 0.69–0.953. The fitting performance between the models is closer for bandgap, with the R2 range 0.941–0.997. The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap. It shows that the nonlinear-ensemble model, constructed by combining multiple weak learners, effectively describes the nonlinear relationship between material features and target property. In addition, the extreme gradient boosting decision tree model shows the most superior results among all the models and searches for two new descriptors that are crucial for formation energy and bandgap. Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems. The dataset that supported the findings of this study is available in Science Data Bank, with the link https://www.doi.org/10.11922/sciencedb.01611.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824 · see citing works
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652 · see citing works
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 75,541 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094 · see citing works
Stranks, S. D., Eperon, G. E., Grancini, G., Menelaou, C., Alcocer, M. J. P., Leijtens, T., Herz, L. M., Petrozza, A., & Snaith, H. J. (2013) · Science · doi:10.1126/science.1243982 · Cited by 10,413 · see citing works
Structural absorption by barbule microstructures of super black bird of paradise feathers
McCoy, D. E., Feo, T., Harvey, T. A., & Prum, R. O. (2018) · Nature Communications · doi:10.1038/s41467-017-02088-w · Cited by 7,424 · see citing works
Electron-hole diffusion lengths > 175 μm in solution-grown CH 3 NH 3 PbI 3 single crystals
Dong, Q., Fang, Y., Shao, Y., Mulligan, P. L., Qiu, J. M., Cao, L. R., & Huang, J. (2015) · Science · doi:10.1126/science.aaa5760 · Cited by 5,649 · see citing works
Angewandte Chemie International Edition
Vazdar, K., Kunetskiy, R. A., Saame, J., Kaupmees, K., Leito, I., & Jahn, U. (2006) · Angewandte Chemie International Edition · doi:10.1002/(issn)1521-3773 · Cited by 5,242 · see citing works
Low trap-state density and long carrier diffusion in organolead trihalide perovskite single crystals
Shi, D., Adinolfi, V., Comin, R., Yuan, M., Alarousu, E., Buin, A., Chen, Y., Hoogland, S., Rothenberger, A., Katsiev, K., et al. (2015) · Science · doi:10.1126/science.aaa2725 · Cited by 5,152 · see citing works
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,339 · see citing works
High-efficiency two-dimensional Ruddlesden–Popper perovskite solar cells
Tsai, H., Nie, W., Blancon, J., Stoumpos, C. C., Asadpour, R., Harutyunyan, B., Neukirch, A. J., Verduzco, R., Crochet, J., Tretiak, S., et al. (2016) · Nature · doi:10.1038/nature18306 · Cited by 3,396 · see citing works
Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828 · see citing works
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657 · see citing works
Machine learning and the physical sciences
Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,453 · see citing works
Unique Properties of Halide Perovskites as Possible Origins of the Superior Solar Cell Performance
Yin, W., Shi, T., & Yan, Y. (2014) · Advanced Materials · doi:10.1002/adma.201306281 · Cited by 2,343 · see citing works
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647 · see citing works
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Bajusz, D., Rácz, A., & Héberger, K. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0069-3 · Cited by 1,646 · see citing works
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069 · see citing works
Hecht-Nielsen, R. (1992) · Elsevier eBooks · doi:10.1016/b978-0-12-741252-8.50010-8 · Cited by 1,003 · see citing works
Design of Lead-Free Inorganic Halide Perovskites for Solar Cells via Cation-Transmutation
Zhao, X., Yang, J., Fu, Y., Yang, D., Xu, Q., Yu, L., Wei, S., & Zhang, L. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b09645 · Cited by 996 · see citing works
Self-citationOuyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.083802 · Cited by 944 · see citing works
Journal of Computational Chemistry
Yu, X. (2006) · Journal of Computational Chemistry · doi:10.1002/(issn)1096-987x · Cited by 927 · see citing works
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 892 · see citing works
Linear Inversion of Band-Limited Reflection Seismograms
Santosa, F., & Symes, W. W. (1986) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0907087 · Cited by 737 · see citing works
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., & Wang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05761-w · Cited by 725 · see citing works
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 636 · see citing works
<scp>G</scp>aussian approximation potentials: A brief tutorial introduction
Bartók, A. P., & Csányi, G. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24927 · Cited by 617 · see citing works
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Cśanyi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 556 · see citing works
Rational Design of Halide Double Perovskites for Optoelectronic Applications
Zhao, X., Yang, D., Ren, J., Sun, Y., Xiao, Z., & Zhang, L. (2018) · Joule · doi:10.1016/j.joule.2018.06.017 · Cited by 509 · see citing works
Self-citationMachine Learning a General-Purpose Interatomic Potential for Silicon
Bartók, A. P., Kermode, J. R., Bernstein, N., & Cśanyi, G. (2018) · Physical Review X · doi:10.1103/physrevx.8.041048 · Cited by 480 · see citing works
Data-Driven Strategies for Accelerated Materials Design
Pollice, R., Gomes, G., Aldeghi, M., Hickman, R. J., Krenn, M., Lavigne, C., Lindner-D’Addario, M., Nigam, A., Ser, C. T., Yao, Z., et al. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00785 · Cited by 477 · see citing works
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446 · see citing works
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A., Ortner, C., Cśanyi, G., Ceriotti, M., et al. (2021) · Cambridge University Engineering Department Publications Database · Cited by 440 · see citing works
The AFLOW standard for high-throughput materials science calculations
Calderón, C. E., Plata, J. J., Toher, C., Oses, C., Levy, O., Fornari, M., Natan, A., Mehl, M. J., Hart, G. L. W., Nardelli, M. B., et al. (2015) · Computational Materials Science · doi:10.1016/j.commatsci.2015.07.019 · Cited by 420 · see citing works
High‐performance flexible sensing devices based on polyaniline/MXene nanocomposites
Zhao, L., Wang, K., Wei, W., Wang, L., & Han, W. (2019) · InfoMat · doi:10.1002/inf2.12032 · Cited by 420 · see citing works
Weng, B., Song, Z., Zhu, R., Yan, Q., Sun, Q., Grice, C. G., Yan, Y., & Yin, W.-J. (2020) · Nature Communications · doi:10.1038/s41467-020-17263-9 · Cited by 376 · see citing works
Machine learning: Accelerating materials development for energy storage and conversion
Chen, A., Zhang, X., & Zhou, Z. P. (2020) · InfoMat · doi:10.1002/inf2.12094 · Cited by 373 · see citing works
Mu, S., Samolyuk, G. D., Wimmer, S., Troparevsky, M. C., Khan, S. N., Mankovsky, S., Ebert, H., & Stocks, G. M. (2018) · npj Computational Materials · doi:10.1038/s41524-018-0138-z · Cited by 334 · see citing works
Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy
Hong, Q., Schroers, J., Hofmann, D. C., Stefano Curtarolo, A., Asta, M., & van de Walle, A. (2021) · npj Computational Materials · doi:10.1038/s41524-020-00473-6 · Cited by 249 · see citing works
Li, Z., Xu, Q., Sun, Q., Hou, Z., & Yin, W. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201807280 · Cited by 233 · see citing works
Innovative Materials Science via Machine Learning
Gao, C., Min, X., Fang, M., Tao, T., Zheng, X., Liu, Y., Wu, X., & Huang, Z. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202108044 · Cited by 198 · see citing works
On the doubt about margin explanation of boosting
Gao, W. D., & Zhou, Z. (2013) · Artificial Intelligence · doi:10.1016/j.artint.2013.07.002 · Cited by 173 · see citing works
Yang, D., Lv, J., Zhao, X., Xu, Q., Fu, Y., Zhan, Y., Zunger, A., & Zhang, L. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b03221 · Cited by 158 · see citing works
Self-citationTalapatra, A., Uberuaga, B. P., Stanek, C. R., & Pilania, G. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c03402 · Cited by 149 · see citing works
Breuck, P.-P. D., Hautier, G., & Rignanese, G. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00552-2 · Cited by 123 · see citing works
Perovskite Solar Absorbers: Materials by Design
Xu, Q., Yang, D., Lv, J., Sun, Y., & Zhang, L. (2018) · Small Methods · doi:10.1002/smtd.201700316 · Cited by 118 · see citing works
Self-citationLyu, R., Moore, C. E., Liu, T., Yu, Y., & Wu, Y. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c05441 · Cited by 114 · see citing works
High‐throughput computational materials screening and discovery of optoelectronic semiconductors
Luo, S., Li, T., Wang, X., Faizan, M., & Zhang, L. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1489 · Cited by 108 · see citing works
Self-citationLarge family of two-dimensional ferroelectric metals discovered via machine learning
Ma, X.-Y., Lyu, H.-Y., Hao, K.-R., Zhao, Y.-M., Qian, X., Yan, Q., & Su, G. (2020) · 中国科学通报：英文版 · doi:10.1016/j.scib.2020.09.010 · Cited by 89 · see citing works
Zhao, X., Zhou, K., Xing, B., Zhao, R., Luo, S., Li, T., Sun, Y., Na, G., Xie, J., Yang, X., et al. (2021) · 中国科学通报：英文版 · doi:10.1016/j.scib.2021.06.011 · Cited by 86 · see citing works
Self-citationReviewing and understanding the stability mechanism of halide perovskite solar cells
Zhang, C., Shen, T., Guo, D., Tang, L., Yang, K., & Deng, H. (2020) · InfoMat · doi:10.1002/inf2.12104 · Cited by 85 · see citing works
Cai, X., Zhang, Y., Shi, Z., Chen, Y., Xia, Y., Yu, A., Xu, Y., Xie, F., Shao, H., Zhu, H., et al. (2022) · Advanced Science · doi:10.1002/advs.202103648 · Cited by 84 · see citing works
Li, T., Luo, S., Wang, X., & Zhang, L. (2021) · Advanced Materials · doi:10.1002/adma.202008574 · Cited by 78 · see citing works
Self-citationMachine Learning Accelerated Recovery of the Cubic Structure in Mixed-Cation Perovskite Thin Films
Ali, A., Park, H., Mall, R., Aïssa, B., Sanvito, S., Bensmail, H., Belaidi, A., & El‐Mellouhi, F. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b05342 · Cited by 61 · see citing works
Computational functionality‐driven design of semiconductors for optoelectronic applications
Liu, Z., Na, G., Tian, F., Yu, L., Li, J., & Zhang, L. (2020) · InfoMat · doi:10.1002/inf2.12099 · Cited by 58 · see citing works
Self-citationMetal Halide Semiconductors beyond Lead-Based Perovskites for Promising Optoelectronic Applications
Wang, X., Li, T., Xing, B., Faizan, M., Biswas, K., & Zhang, L. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c02877 · Cited by 54 · see citing works
Self-citationKireeva, N. V., & Pervov, V. S. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp00518k · Cited by 53 · see citing works
Design of Mixed‐Cation Tri‐Layered Pb‐Free Halide Perovskites for Optoelectronic Applications
Liu, Z., Zhao, X., Zunger, A., & Zhang, L. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900234 · Cited by 27 · see citing works
Self-citationImpact of organic molecule rotation on the optoelectronic properties of hybrid halide perovskites
Xu, Q., Stroppa, A., Lv, J., Zhao, X., Yang, D., Biswas, K., & Zhang, L. (2019) · Physical Review Materials · doi:10.1103/physrevmaterials.3.125401 · Cited by 25 · see citing works
Self-citationTheoretical investigation of halide perovskites for solar cell and optoelectronic applications*
Yang, J., Zhang, P., Wang, J., & Wei, S. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abb3f6 · Cited by 18 · see citing works
Grain Boundaries in Methylammonium Lead Halide Perovskites Facilitate Water Diffusion
Tian, F., Feng, W., Xing, B., He, X. H., Saidi, W. A., & Zhang, L. (2021) · Advanced Energy and Sustainability Research · doi:10.1002/aesr.202100087 · Cited by 18 · see citing works
Self-citationYang, D., Fu, Y., Sun, Y., Li, Y., Wang, K., Xiao, Z., Biswas, K., & Zhang, L. (2021) · Physical Review Materials · doi:10.1103/physrevmaterials.5.054603 · Cited by 16 · see citing works
Self-citationStructural, Thermodynamical and Electronic Properties of All-Inorganic Lead Halide Perovskites
Li, Y., Na, G., Luo, S., He, X. H., & Zhang, L. (2020) · Acta Physico-Chimica Sinica · doi:10.3866/pku.whxb202007015 · Cited by 15 · see citing works
Self-citationWang, X., Fu, Y., Na, G., Li, H., & Zhang, L. (2019) · Acta Physica Sinica · doi:10.7498/aps.68.20190596 · Cited by 14 · see citing works
Self-citationZhao, G., Xie, J., Zhou, K., Xing, B., Wang, X., Tian, F., He, X. H., & Zhang, L. (2021) · Chinese Physics B · doi:10.1088/1674-1056/ac4036 · Cited by 12 · see citing works
Self-citationRecent research progress on optimal design of halide perovskite photovoltaic materials
Dian-long, 赵. Z., Tian-shu, 李. L., Qiao-ling, 徐. X., Xue-ting, 王. W., & Li-jun, 张. Z. (2019) · Chinese Optics · doi:10.3788/co.20191205.0964 · Cited by 10 · see citing works
Designing solar-cell absorber materials through computational high-throughput screening*
Jiang, X., & Yin, W. (2019) · Chinese Physics B · doi:10.1088/1674-1056/ab6655 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- [14]14. Bartók A P and Csányi G 2015 International Journal of Quantum Chemistry 115 1051
- [19]19. Bajusz D, Rácz A and Héberger K 2015 Journal of Cheminformatics 7 20
- [28]28. Tibshirani R 1996 Journal of the Royal Statistical Society: Series B (Methodological) 58 267
- [53]53. Ma X Y, Lyu H Y, Hao K R, Zhao Y M, Qian X, Yan Q B and Su G 2021 Science Bulletin 66 233
- [64]64. Zhao X G, Zhou K, Xing B, Zhao R, Luo S, Li T, Sun Y, Na G, Xie J, Yang X, Wang X, Wang X, He X, Lv J, Fu Y and Zhang L 2021 Science Bulletin 66 1973
- [65]65. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M and Duchesnay é 2011 Journal of Machine Learning Research 12 2825
- [66]66. Chen T and Guestrin C 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp. 785-794
- [67]67. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q and Liu T Y 2017 Advances in Neural Information Processing Systems (Vol. 30)
- [68]68. Bergstra J, Yamins D and Cox D 2013 Proceedings of the 30th Inter- national Conference on Machine Learning pp. 115-23
- [76]76. Hecht-nielsen R 1992 Neural Networks for Perception (ed H Wechsler) pp. 65-93
Cited by
Sorted by citations
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214271 · Cited by 174 · see citing works
MIC-SHAP: An ensemble feature selection method for materials machine learning
Wang, J., Xu, P., Ji, X., Li, M., & Lu, W. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.106910 · Cited by 36 · see citing works
Xie, J., Zhou, Y., Faizan, M., Li, Z., Li, T., Fu, Y., Wang, X., & Zhang, L. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00632-5 · Cited by 37 · see citing works
Self-citationEntropy-Driven Stabilization of Multielement Halide Double-Perovskite Alloys
Wang, X., Yang, J., Wang, X., Faizan, M., Zou, H., Zhou, K., Xing, B., Fu, Y., & Zhang, L. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01180 · Cited by 27 · see citing works
Self-citationOkumura, R., Oku, T., & Suzuki, A. (2023) · Nano Trends · doi:10.1016/j.nwnano.2023.100020 · Cited by 23 · see citing works
Machine-learning-assisted discovery of perovskite materials with high dielectric breakdown strength
Li, J., Peng, Y., Zhao, L., Chen, G., Zeng, L., Wei, G., & Xu, Y. (2022) · Materials Advances · doi:10.1039/d2ma00839d · Cited by 20 · see citing works
Role of self-assembled molecules in halide perovskite optoelectronics: an atomic-scale perspective
Wang, X., Wang, X., Wang, X., Li, M., Li, H., Fu, Y., & Zhang, L. (2025) · National Science Review · doi:10.1093/nsr/nwaf150 · Cited by 18 · see citing works
Self-citationRamiah, K., & Pitchipoo, P. (2023) · Materials Research Express · doi:10.1088/2053-1591/acb909 · Cited by 16 · see citing works
Machine‐Learning Accelerating the Development of Perovskite Photovoltaics
Liu, T., Wang, S., Shi, Y., Wu, L., Zhu, R., Wang, Y., Zhou, J., & Choy, W. C. H. (2023) · Solar RRL · doi:10.1002/solr.202300650 · Cited by 14 · see citing works
Ren, C., Wu, Y., Zou, J., & Cai, B. (2024) · Materials · doi:10.3390/ma17112686 · Cited by 12 · see citing works
Zhang, Y., Mu, H., Cai, Y., Wang, X., Zhou, K., Tian, F., Fu, Y., & Zhang, L. (2023) · Chinese Physics B · doi:10.1088/1674-1056/accdca · Cited by 5 · see citing works
Self-citationZorkun, N. G., Öncül, M. H. M., Karatay, S., Erken, F., & Budak, C. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.06.080 · Cited by 4 · see citing works
Ouali, M. A., Bennacer, H., Ladjal, M., & Bouzateur, I. (2025) · Computational Materials Science · doi:10.1016/j.commatsci.2025.114397 · Cited by 3 · see citing works
Wang, B., Wang, J., Li, L., & Wang, D. (2026) · Frontiers in Materials · doi:10.3389/fmats.2025.1669229 · Cited by 3 · see citing works
Zhang, J., Liu, Y.-M., & Tu, Z. C. (2021) · Chinese Physics B · doi:10.1088/1674-1056/ac8d88 · Cited by 1 · see citing works
Hu, J., Wu, M., & Ren, J. (2025) · Physical Review Materials · doi:10.1103/l5mw-h2m7
Bykov, A. V., & Shevelkov, А. V. (2026) · Chemistry of Materials · doi:10.1021/acs.chemmater.6c00619
Machine learning assisted development of lead-free Cs3Bi2I9 perovskite solar cells
Choudhary, A., Rumman, A. H., Sahriar, M. A., Islam, M. T., Ahmed, S., & Efstathiadis, H. (2026) · Solar Energy · doi:10.1016/j.solener.2026.114743
Zhou, Y., Zhao, X., Zhou, K., Fu, Y., Wang, X., Li, Y., Jian, M., He, X., Singh, D. J., & Zhang, L. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.05.072
Self-citationSong, Y., & Liu, Y. (2025) · International Conference on Applied Statistics, Modeling and Advanced Algorithms (ASMA) · doi:10.1109/asma67812.2025.11553028
AI-driven analysis of harmonic intensity in perovskite single crystals
Khanam, S. J., Kuruva, H., Konda, S. R., Moiz, S. A., Savu, R. N., & Murali, B. (2026) · Journal of Materials Chemistry C · doi:10.1039/d6tc02704k
- Authors
- Zhao, R., Xing, B., Mu, H., Fu, Y., & Zhang, L.
- Affiliations
- Jilin University (CN)
- Corresponding author
- Zhang, Lijun
- Venue
- Chinese Physics B
- Publisher
- IOP Publishing
- Published
- 2022-05-01
- Volume
- 31
- Issue
- 5
- Pages
- 056302
- ISSN
- 1674-1056 (ISSN-L)
- OpenAlex
- W4221136798
- Cited by
- 21
- References
- 71
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 2 · 2023: 6 · 2022: 3
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Chinese Physics B (publishedVersion)
- Funders
- National Natural Science Foundation of China (92061113, 62125402)
- Jilin University
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- halide perovskites, classifier comparison, structure-property relationships, formation energy prediction, band gap prediction, ensemble learning, extreme gradient boosting, material descriptors, materials informatics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
