Curie Temperature Prediction of BiFeO3-PbTiO3-BaTiO3 Solid Solution Based on Machine Learning

Zhixiang JIAO; Fanhao JIA; Yongchen WANG; Jianguo CHEN; Wei REN; Jinrong CHENG

doi:10.15541/jim20220080

[1] J CHEN, Y QI, G SHI et al. A high temperature piezoelectric ceramic: (1-x)(Bi0.9La0.1)FeO3-xPbTiO3 crystalline solutions. IEEE Trans. Ultrason. Ferroelectr. Freq. Control., 1820-1825(2009). http://ieeexplore.ieee.org/document/5278429/

[2] J CHEN, D JIN, J CHENG. Impedance spectroscopy studies of 0.7Bi(Fe1-xGax)O3-0.3PbTiO3 high temperature piezoelectric ceramics. J. Alloys Compd., 67-71(2013). https://linkinghub.elsevier.com/retrieve/pii/S0925838813009596

[3] J CHENG, S YU, J CHEN et al. Dielectric and magnetic enhancements in BiFeO3-PbTiO3 solid solutions with La doping. Appl. Phys. Lett., 122911(2006). http://aip.scitation.org/doi/10.1063/1.2353806

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

[6] K MURPHY. Machine learning:a probabilistic perspective, 71(2012).

[7] F PEDREGOSA, G VAROQUAUX, A GRAMFORT et al. Scikit- learn: machine learning in python. Journal of Machine Learning Research, 2825-2830(2011).

[8] M RUPP, A TKATCHENKO, K MÜLLER et al. Fast and accurate modeling of molecular atomization energies with machine learning. Phy. Rev. Lett., 058301(2012). https://link.aps.org/doi/10.1103/PhysRevLett.108.058301

[9] M JORDAN, T MITCHELL. Machine learning: trends, perspectives, and prospects. Science, 255-260(2015).

[10] M ZHONG, K TRAN, Y MIN et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature, 178-183(2020). https://doi.org/10.1038/s41586-020-2242-8

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

[12] G RANDHAWA, K HILL, L KARI. ML-DSP: Machine learning with digital signal processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels. BioMed Central, 267-23(2019).

[13] Z CHENG, E ZHU, N CHEN. Application of orthogonal expansion to mapping and modelling. Chemometr., 243-253(1993). https://onlinelibrary.wiley.com/doi/10.1002/cem.1180070403

[14] N CHEN, C LI, P QIN. Chemical pattern recognition applied to materials optimal design and industry optimization. Chin. Sci. Bull., 793-799(1997). http://link.springer.com/10.1007/BF02882484

[15] N CHEN, W LU, R CHEN et al. Chemometric methods applied to industrial optimization and materials optimal design. Chemom. Intel. Lab. Syst., 329-333(1999). https://linkinghub.elsevier.com/retrieve/pii/S0169743998001397

[16] N CHEN, D ZHU, W WANG. Intelligent materials processing by hyperspace data mining. Eng. Appl. Artif. Intel., 527-532(2000). https://linkinghub.elsevier.com/retrieve/pii/S0952197600000324

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

[18] R ARMIENTO, B KOZINSKY, M FORNARI et al. Screening for high-performance piezoelectrics using high-throughput density functional theory. Physics Review B, 014103(2011). https://link.aps.org/doi/10.1103/PhysRevB.84.014103

[19] V PRASANNA, K BENJAMIN, S ALP et al. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nature Communications, 1668(2018).

[20] M EVAN, Y SUHAS, G ILYA. Prediction of the Curie temperatures of ferroelectric solid solutions using machine learning methods. Computational Materials Science, 110730(2021). https://linkinghub.elsevier.com/retrieve/pii/S0927025621004572

[21] R OUYANG, S CURTAROLO, E AHMETCIK et al. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater., 083802(2018).

[22] R OUYANG, E AHMETCIK, C CARBOGNO et al. Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO. J. Phys.: Mater., 024002(2019). https://iopscience.iop.org/article/10.1088/2515-7639/ab077b

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

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

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

[26] J CHRISTOPHER, S CHRISTOPHER, R BRYAN et al. New tolerance factor to predict the stability of perovskite oxides and halides. Science Advances, eaav0693(2019). https://www.science.org/doi/10.1126/sciadv.aav0693

[27] K BUTLER, J FROST, J SKELTON et al. Computational materials design of crystalline solids. Che. Soc. Rev., 6138-6146(2016).

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

[29] E UUSI, J MALM, N IMAMURA et al. Characterization of RMnO3 (R=Sc, Y, Dy-Lu): high-pressure synthesized metastable perovskites and their hexagonal precursor phases. Materials Chemistry & Physics, 1029-1034(2008).

[30] P SCHOBER, C BOER, L SCHWARTE. Correlation coefficients: appropriate use and interpretation. Anesthesia and Analgesia, 1763-1768(2018).

[31] X YANG, M LI, Q SU et al. QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes. Medicinal Chemistry Research, 5274-5283(2013). http://link.springer.com/10.1007/s00044-013-0527-2