Data Mining of Constitutive Relationships in Ferroelectricity
Based on Machine Learning
Yang Bai1,2,*, Jingjin He1,2, Junjie Li1,2, Changxin Wang1,2, Yan Zhang 1,2, Cheng Wen1,2, Dezhen Xue3,* Yanjing Su1,2, Lijie Qiao1,2
1 Beijing Advanced Innovation Center for
Materials Genome Engineering, University of Science and Technology Beijing,
Beijing 100083, China
2 Institute
for Advanced Material and Technology, University of Science and Technology
Beijing, Beijing 100083, China
3 School of Materials Science
and Technology, University of Science and Technology Beijing, Beijing 100083,
China
4 State
Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University,
Xi'an 710049, China
ABSTRACT: With aid of good
materials descriptors, machine learning algorithms are able to accelerate the
design of new materials and uncover underlying mechanisms. In the present
study, we adopt machine learning methods to discover dominant materials
descriptors for intrinsic feature of ferroelectric materials. A regression
study, in typical BaTiO3-based and K1/2Na1/2NbO3-based lead-free ceramics and
lead-contained PbMg1/3Nb2/3TiO3-PbTiO3 ceramics, screens out three important
materials descriptors determining ferroelectricity from 46 potential
descriptors. The three descriptors of Matyonov-Batsanov electronegativity, ratio
of valence electron number to nominal charge and core electron distance
(Schubert) are confirmed to be dominant as well in classification of perovskite
compounds into antiferroelectrics or not. The classification based on these
descriptors exhibit an excellent accuracy of 96%, much higher than that of traditional
criterion (89%) using tolerance factor and Pauling electronegativity.
Furthermore, we propose a machine learning strategy based on our descriptors to
predict the phase coexistence. The prediction probability after bootstrapping
provides an effective approach to distinguish the phase boundaries and predict
the phase ratio of coexisted phases. In all, we identified materials
descriptors for ferroelectric materials, which is helpful to reveal the structure-property
relationship of ferroelectric materials and guide the design of better
ferroelectricity and piezoelectricity.
Keywords: Ferroelectricity; Machine learning; Materials descriptors
Yang Bai is a Professor in the Institute for Advanced Materials and Technology at University of Science and Technology Beijing (China). He received his B.S. and Ph.D. in Materials Science and Engineering from Tsinghua University, in 2001 and 2006. Since 2006, he works in University of Science and Technology Beijing. In 2012, he was voted as the National Program for Support of Top-Notch Young Professionals, and was selected in the New Century Excellent Talents plan (MOE). In 2013, he was selected in the Beijing Higher Education Young Elite Teacher Project. In 2015, he was voted as the Best Scientific Research Workers by the Chinese Association of Young Scientists and Technologists. In 2019, he was voted the 13th Youth Science and Technology Award of the Chinese Ceramic Society. He is a fellow in the Advanced Ceramics Society of Chinese Ceramic Society, and the Instrument Functional Materials Society of China Instrument and Control Society China. He serves as advisory panel in Journal of Physics D: Applied Physics, editor in Journal of Advanced Ceramics, SCIENCE CHINA: Technological Sciences, and International Journal of Minerals, Metallurgy and Materials. He also serves as peer reviewer of more than 80 international academic journals including Adv Mater, Mater Today, Mater Horiz, J Am Ceram Soc, and so on.