[1] CHENJ, QIY, SHIG, et al.A high temperature piezoelectric ceramic: (1-x)(Bi0.9La0.1)FeO3-xPbTiO3 crystalline solutions. IEEE Trans. Ultrason. Ferroelectr. Freq. Control., 2009, 56(9):1820-1825. 10.1109/TUFFC.2009.1255http://ieeexplore.ieee.org/document/5278429/

[2] CHENJ, JIND, CHENGJ.Impedance spectroscopy studies of 0.7Bi(Fe1-xGax)O3-0.3PbTiO3 high temperature piezoelectric ceramics. J. Alloys Compd., 2013, 580:67-71. 10.1016/j.jallcom.2013.04.076https://linkinghub.elsevier.com/retrieve/pii/S0925838813009596

[3] CHENGJ, YUS, CHENJ, et al.Dielectric and magnetic enhancements in BiFeO3-PbTiO3 solid solutions with La doping. Appl. Phys. Lett., 2006, 89(12):122911. 10.1063/1.2353806http://aip.scitation.org/doi/10.1063/1.2353806

[4] CHENJ, CHENGJ.Enhanced high-field strain and reduced high- temperature dielectric loss in 0.6(Bi0.9La0.1)(Fe1-xTix)O3-0.4PbTiO3 piezoelectric. Ceram. Int., 2015, 41(1):1617-1621. 10.1016/j.ceramint.2014.09.099https://linkinghub.elsevier.com/retrieve/pii/S0272884214014849

[5] 谢颖.ABO3型钙钛矿的相变机理表面稳定性和电子结构的理论研究. 哈尔滨: 黑龙江大学出版社, 2015.

[6] MURPHYK.Machine learning:a probabilistic perspective. Cambridge: The MIT Press, 2012, 58(8):27-71.

[7] PEDREGOSAF, VAROQUAUXG, GRAMFORTA, et al.Scikit- learn: machine learning in python. Journal of Machine Learning Research, 2011, 12(10):2825-2830.

[8] RUPPM, TKATCHENKOA, MÜLLERK, et al.Fast and accurate modeling of molecular atomization energies with machine learning. Phy. Rev. Lett., 2012, 108(5):058301. 10.1103/PhysRevLett.108.058301https://link.aps.org/doi/10.1103/PhysRevLett.108.058301

[9] JORDANM, MITCHELLT.Machine learning: trends, perspectives, and prospects. Science, 2015, 349(6245):255-260. 10.1126/science.aaa841526185243Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

[10] ZHONGM, TRANK, MINY, et al.Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature, 2020, 581(7807):178-183. 10.1038/s41586-020-2242-8https://doi.org/10.1038/s41586-020-2242-8

[11] BATRAR.Accurate machine learning in materials science facilitated by using diverse data sources. Nature, 2021, 589(7843):524-525. 10.1038/d41586-020-03259-4https://doi.org/10.1038/d41586-020-03259-4

[12] RANDHAWAG, HILLK, KARIL.ML-DSP: Machine learning with digital signal processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels. BioMed Central, 2019, 20(1):267-23.

[13] CHENGZ, ZHUE, CHENN.Application of orthogonal expansion to mapping and modelling. Chemometr., 1993, 7(4):243-253. 10.1002/cem.1180070403https://onlinelibrary.wiley.com/doi/10.1002/cem.1180070403

[14] CHENN, LIC, QINP.Chemical pattern recognition applied to materials optimal design and industry optimization. Chin. Sci. Bull., 1997, 42(10):793-799. 10.1007/BF02882484http://link.springer.com/10.1007/BF02882484

[15] CHENN, LUW, CHENR, et al.Chemometric methods applied to industrial optimization and materials optimal design. Chemom. Intel. Lab. Syst., 1999, 45(1):329-333. 10.1016/S0169-7439(98)00139-7https://linkinghub.elsevier.com/retrieve/pii/S0169743998001397

[16] CHENN, ZHUD, WANGW.Intelligent materials processing by hyperspace data mining. Eng. Appl. Artif. Intel., 2000, 13(5):527-532. 10.1016/S0952-1976(00)00032-4https://linkinghub.elsevier.com/retrieve/pii/S0952197600000324

[17] RESTAR.Macroscopic polarization in crystalline dielectrics: the geometric phase approach. Review Modern Physics, 1994, 66(3):899-916. 10.1103/RevModPhys.66.899https://link.aps.org/doi/10.1103/RevModPhys.66.899

[18] ARMIENTOR, KOZINSKYB, FORNARIM, et al.Screening for high-performance piezoelectrics using high-throughput density functional theory. Physics Review B, 2011, 84(1):014103. 10.1103/PhysRevB.84.014103https://link.aps.org/doi/10.1103/PhysRevB.84.014103

[19] PRASANNAV, BENJAMINK, ALPS, et al.Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nature Communications, 2018, 9:1668. 10.1038/s41467-018-03821-929700297Experimental search for high-temperature ferroelectric perovskites is a challenging task due to the vast chemical space and lack of predictive guidelines. Here, we demonstrate a two-step machine learning approach to guide experiments in search of xBi[Me'Me-y ''((1-y))]O-3-(1 - x) PbTiO3-based perovskites with high ferroelectric Curie temperature. These involve classification learning to screen for compositions in the perovskite structures, and regression coupled to active learning to identify promising perovskites for synthesis and feedback. The problem is challenging because the search space is vast, spanning similar to 61,500 compositions and only 167 are experimentally studied. Furthermore, not every composition can be synthesized in the perovskite phase. In this work, we predict x, y, Me', and Me '' such that the resulting compositions have both high Curie temperature and form in the perovskite structure. Outcomes from both successful and failed experiments then iteratively refine the machine learning models via an active learning loop. Our approach finds six perovskites out of ten compositions synthesized, including three previously unexplored {Me'Me ''} pairs, with 0.2Bi (Fe0.12Co0.88)O-3-0.8PbTiO(3) showing the highest measured Curie temperature of 898 K among them.

[20] EVANM, SUHASY, ILYAG.Prediction of the Curie temperatures of ferroelectric solid solutions using machine learning methods. Computational Materials Science, 2021, 199(7061):110730. 10.1016/j.commatsci.2021.110730https://linkinghub.elsevier.com/retrieve/pii/S0927025621004572

[21] OUYANGR, CURTAROLOS, AHMETCIKE, et al.SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater., 2018, 2(8):083802.

[22] OUYANGR, AHMETCIKE, CARBOGNOC, et al.Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO. J. Phys.: Mater., 2019, 2(2):024002. 10.1088/2515-7639/ab077bhttps://iopscience.iop.org/article/10.1088/2515-7639/ab077b

[23] OUYANGR.Exploiting ionic radii for rational design of halide perovskites. Chem. Mater., 2020, 32(1):595-604. 10.1021/acs.chemmater.9b04472https://pubs.acs.org/doi/10.1021/acs.chemmater.9b04472

[24] NINGZ.Dielectric, ferroelectric, piezoelectric and aging properties of BiFeO3-PbTiO3-BaTiO3 high temperature piezoelectric ceramics. Shanghai: Master Thesis, Shanghai University, 2020.

[25] TUT.Fabrication of BF-PT-BT high temperature piezoelectric ceramics and sensors. Shanghai: Master Thesis, Shanghai University, 2017.

[26] CHRISTOPHERJ, CHRISTOPHERS, BRYANR, et al.New tolerance factor to predict the stability of perovskite oxides and halides. Science Advances, 2019, 5(2):eaav0693. 10.1126/sciadv.aav0693https://www.science.org/doi/10.1126/sciadv.aav0693

[27] BUTLERK, FROSTJ, SKELTONJ, et al.Computational materials design of crystalline solids. Che. Soc. Rev., 2016, 45(22):6138-6146.

[28] YUJ, ITOHM.Physics-guided data-mining driven design of room- temperature multiferroic perovskite oxides. Phys. Status Solidi RRL, 2019, 13(6):1900028. 10.1002/pssr.201900028https://onlinelibrary.wiley.com/doi/10.1002/pssr.201900028

[29] UUSIE, MALMJ, IMAMURAN, et al.Characterization of RMnO3 (R=Sc, Y, Dy-Lu): high-pressure synthesized metastable perovskites and their hexagonal precursor phases. Materials Chemistry & Physics, 2008, 112(3):1029-1034.

[30] SCHOBERP, BOERC, SCHWARTEL.Correlation coefficients: appropriate use and interpretation. Anesthesia and Analgesia, 2018, 126(5):1763-1768. 10.1213/ANE.000000000000286429481436Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.

[31] YANGX, LIM, SUQ, et al.QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes. Medicinal Chemistry Research, 2013, 22(11):5274-5283. 10.1007/s00044-013-0527-2http://link.springer.com/10.1007/s00044-013-0527-2