Designing High Dielectric Permittivity Material in Barium Titanate - The

Article

Designing High Dielectric Permittivity Material in Barium Titanate Jinghui Gao, Yongbin Liu, Yan Wang, Xinghao Hu, Wenbo Yan, Xiaoqin Ke, Lisheng Zhong, Yuting He, and Xiaobing Ren J. Phys. Chem. C, Just Accepted Manuscript • Publication Date (Web): 31 May 2017 Downloaded from http://pubs.acs.org on June 4, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

The Journal of Physical Chemistry C is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The Journal of Physical Chemistry

Designing High Dielectric Permittivity Material in Barium Titanate Jinghui Gao1, Yongbin Liu1, Yan Wang1, Xinghao Hu1, Wenbo Yan1, Xiaoqin Ke2*, Lisheng Zhong1* , Yuting He1, Xiaobing Ren1,3*

1. State Key Laboratory of Electrical Insulation and Power Equipment and Multidisciplinary Materials Research Center, Frontier Institute of Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China 2. Center of Microstructure Science, Frontier Institute of Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China 3. Ferroic Physics Group, National Institute for Materials Science, Tsukuba, 3050047, Ibaraki, Japan

1

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract Developing high dielectric permittivity material is vital to satisfy the ongoing demands for the miniaturization of electronic and energy storage devices. Recent investigations uncover the role of a thermodynamical tricritical phenomenon on enhancing the dielectric response. However, such a tricritical point always locates in an extremely narrow composition region, which makes it time-consuming for exhaustive experimental searching of the optimal dielectric permittivity in a given material system. In the present paper, we employ an accelerated discovery strategy to seek the largest dielectric permittivity in Ba(Ti1-x%Hfx%)O3 ceramic material by using iterative method between the computational machine learning and experimental synthesis, property measurement. The optimal composition is found to be x=11 with the highest permittivity of εr=4.5 × 104 after 4 loops of iteration involving 6 compositions, which shows higher efficiency compared with conventional experimental searching. Further thermal analysis study suggests that such a permittivity-maximum location on the phase diagram is indeed a tricritical point. Moreover, the microstructure investigation by TEM observation indicates that the tricritical point shows a mottled morphology consisting of numerous nanodomains with multi-phase coexisting. And a phenomenological thermodynamic model based on the experimental result implies that the tricriticality is responsible for the enhanced dielectric permittivity.

Corresponding Authors: *E- mail: [email protected]. Tel: +86-029-83395072 *E- mail: [email protected]. Tel: +86-029-82668268 *E- mail: [email protected]. Tel: +81-29-859-2731 2


Page 2 of 32

Page 3 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1. Introduction Miniaturization for devices has been becoming a trend in the electronic products industry, which enables the promotion of portability and space saving capability. Accordingly, a large number of integrated components have been employed in the manufacture of electronic devices. And the ongoing demand for the high degree of integration has been challenging the material properties and its design. For example, due to the high compactness of power-capacitive equipments and correlated devices in the electric system1-3, it is urgent to design and develop materials with large dielectric permittivity that can store more electrical energy in a constrained material volume. BaTiO3-based ferroelectric ceramics exhibit high dielectric permittivity around ferroelectric-paraelectric transition temperature (TC)4,5, which has been widely used in the capacitors, and the phase coexistence plays an important role for dielectric response as well as energy storage properties6-20. However, the strong dielectric response (and piezoelectric response as well) due to ferroelectric transition is always deficient in thermal reliability21,22, since the permittivity changes abruptly in the vicinity of TC. Therefore, a number of approaches have been taken in order to enhance the temperature stability and meet the industrial requirements23,24. For example, adding TC depressors (MgZrO3,CaTiO3 etc.) can reduce the sharpness of the temperature-dependence of dielectric permittivity peak24,25. Also, the reduced grain size and chemical inhomogeneity (as well as relaxor ferroelectrics) are capable to promote the dielectric thermal reliability25-30. These are always at the expense of the reduction of dielectric permittivity value. Regardless of these material modification strategies, the dielectric anomaly around TC imposes the upper limitation for 3



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 32

permittivity of the materials, and is thus vital for the design of high-permittivity material. Large dielectric response has been reported around the Curie temperatures in specific composition region for Ba(Ti1-x%Mx%)O3 ferroelectric ceramics 31-36, where M can be chose as Zr4+, Sn4+, Hf4+ ,Ce4+ showing homovalence and comparable size with Ti4+. Recent investigations further pointed out that strong dielectric response in these compositions can be ascribed to a thermodynamically-special tricritical point or invariant critical point37-43, which facilitates the potential applications on energy storage or electrocaloric devices33,34. However, the tricritical behavior only occurs in an extremely narrow composition region, and finding the optimized dielectric property usually requires intensive work on material synthesis and property measurements

with

trial-and-error

testing.

Even

though

high-permittivity

compositions have been reported for some of the Ba(Ti1-x%Mx%)O3 ferroelectric ceramics34, it is still lack of the accelerated searching method to hunt the tricritical composition with optimal dielectric response. Recent development of informatics enables the timesaving and simplification for the materials discovery process based on effective mining from available data and knowledge44-48. Very recent investigations by D. Xue, P. V. Balachanran, T. Lookman et. al. proposed an efficient approach on accelerated searching for material with targeted property for ferroic materials by means of a computational machine learning method49-52. D. Xue et. al. reported a material design method to find the lowest thermal hysteresis in a ferroelastic system, i.e. shape memory alloy49. P. V. Balachanran et. al. further studied the crystal structure-TC relationship of ferroelectric material by using machine learning in combination with density function theory calculation50. These studies have verified the possibility to predict the performance 4


Page 5 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and accelerate the discovery process by using machine learning approach in ferroelectric materials. In the present work, we employ an expedited discovery strategy to seek for the optimized dielectric permittivity of Ba(Ti1-x%Hfx%)O3 (BTH-x) ceramic based on a machine learning material design method. The specimen with largest permittivity has been experimentally synthesized, and the corresponding dielectric property has been characterized. The possible reason for the enhancement of dielectric response has been proposed by performing further thermal analysis as well as the microstructure observation, and the associated model based on the experimental results has been proposed. Our work suggests that BTH-x ceramic can be considered as a promising high permittivity dielectric ceramic, which might be utilized on capacitors and energy storage devices in the future. 2. Methods 2.1 The framework In the present work, an accelerated exploring strategy has been adopted for the material design of high permittivity ferroelectric material, and the framework in Fig. 1 shows the flow chart for the designing of Ba(Ti1-x%Hfx%)O3 (BTH-x) ceramics with targeted high-permittivity property. The initial dataset consists of data from dielectric measurement of several compositions. After performing a computational machine learning procedure, the composition with highest possibility of optimal property was predicted. And then fabrication and property measurement were experimentally performed on the predicted composition, which produced new data that could be added into dataset for further iteration. The iterative searching has been performed until it successfully meets the condition of convergence. When the best dielectric 5



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 32

permittivity has been successfully obtained, further material characterizations and modeling analysis were performed in order to understand the underlying mechanism for large dielectric permittivity. 2.2 Material prediction using machine learning Machine learning describes a group of algorithms that can learn from data and further enables the prediction or decision. Before any prediction, several initial data should be measured and put into the dataset. Therefore, we fabricated a series of BTH-x ceramics with equal-interval compositions of x=3, 9, 15 covering the normal ferroelectric composition range, and the dielectric permittivity peak values for different compositions were measured through dielectric thermal spectrum. The composition and its corresponding permittivity peak value were put into dataset. In order to make prediction from the data, we employed a machine learning approach to make a Gaussian process model (GPM) regression53 of composition-permittivity relationship, which is known as “regressor”. And then, a selector was used to choose next location for composition . Here, we used an efficient global optimization (EGO) selector54, which is based on the maximization of the expected improvement, abbreviated as EI . EI =

− Φ + > 0 0 = 0

, where Z =

(1)

. µ(x) and σ(x) are the mean and root-mean-square error of

GPM regression, and Φ(Z) and φ(Z) are the probability distribution function and probability density function.

f(x+) is the best observation which refers to the

maximum measured permittivity in the dataset. In the next step, the predicted composition using EGO in previous iteration was experimentally synthesized, and its dielectric permittivity was measured and put into dataset for further iteration. The 6


Page 7 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


iteration ended until the expected improvement was less than 1% of the current best observation (with the condition of convergence, EI

Designing High Dielectric Permittivity Material in Barium Titanate - The

Recommend Documents