Oxide ceramics of A 2 M 3 O 12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics

被引:5
作者
Kireeva, Natalia [1 ]
Tsivadze, Aslan Yu. [1 ]
机构
[1] Frumkin Inst Phys Chem & Electrochem RAS, Leninsky Prospect 31, Moscow 119071, Russia
关键词
negative thermal expansion; ferroelastic phase transition; linear thermal expansion coefficient; phase transition temperature; machine learning; LITHIUM INSERTION; CHEMISTRY; INTERCALATION; CONDUCTIVITY; MGHF(WO4)(3); FE2(MOO4)3; IMPUTATION; DISCOVERY; ZRW2O8; ANION;
D O I
10.1016/j.jallcom.2024.174356
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Ceramic materials with negative and close -to -zero coefficients of thermal expansion may open the avenue to the technologies that have so far been constrained by physical limitations concerned with the thermal stress or with the insufficient structural stability. Two important characteristics of NTE materials which could be used for the evaluation of the possible area and limitations of the sphere of application for negative thermal expansion (NTE) materials are the linear thermal expansion coefficient and the transition temperature from monoclinic to orthorhombic phase. In this study, the machine learning methods were involved in the analysis of experimental data for NTE oxide ceramics of A 2 M 3 O 12 family (where M is Mo 6 + , W 6 + , V 5 + or P 5 + while A position may be accommodated by the wide range of metal cations). The models are characterized by the following statistical coefficients: the determination coefficient R 2 = 0.81 and prediction error RMSE = 1.170 for linear thermal expansion coefficient; the corresponding parameters for the phase transition temperature were assessed as 0.81 and 82.239, respectively. Ionic conductivity in this class of compounds has been discussed as a tandem functional characteristic, emphasizing the role of anharmonicity in both characteristics. The role of synthesis route and defect chemistry in NTE was analyzed. A conclusion on the expected enhancement of NTE resulted from the intentional introduction of cation A vacancies has been made. The principal possibility of combining two functional characteristics, - an ion conductor and a phase with negative thermal expansion, with some compromise in the characteristics of each of them is substantiated.
引用
收藏
页数:15
相关论文
共 123 条
[1]   Ionic conducting lanthanide oxides [J].
Adachi, GY ;
Imanaka, N ;
Tamura, S .
CHEMICAL REVIEWS, 2002, 102 (06) :2405-2429
[2]  
[Anonymous], 2024, Database of properties of chemical elements
[3]   Synthesis of MgHf(WO4)3 and MgZr(WO4)3 using a non-hydrolytic sol-gel method [J].
Baiz, Tamam I. ;
Gindhart, Amy M. ;
Kraemer, Shannon K. ;
Lind, Cora .
JOURNAL OF SOL-GEL SCIENCE AND TECHNOLOGY, 2008, 47 (02) :128-130
[4]   THERMAL EXPANSION OF SILVER IODIDE [J].
BIENENSTOCK, A ;
BURLEY, G .
JOURNAL OF PHYSICS AND CHEMISTRY OF SOLIDS, 1963, 24 (11) :1271-&
[5]   Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV [J].
Borvick, Elana ;
Anderson, Assaf Y. ;
Barad, Hannah-Noa ;
Priel, Maayan ;
Keller, David A. ;
Ginsburg, Adam ;
Rietwyk, Kevin J. ;
Meir, Simcha ;
Zaban, Arie .
ACS COMBINATORIAL SCIENCE, 2017, 19 (12) :755-762
[6]   Anharmonic host-lattice dynamics enable fast ion conduction in superionic AgI [J].
Brenner, Thomas M. ;
Gehrmann, Christian ;
Korobko, Roman ;
Livneh, Tsachi ;
Egger, David A. ;
Yaffe, Omer .
PHYSICAL REVIEW MATERIALS, 2020, 4 (11)
[7]   Machine Learning in Nanoscience: Big Data at Small Scales [J].
Brown, Keith A. ;
Brittman, Sarah ;
Maccaferri, Nicolo ;
Jariwala, Deep ;
Ceano, Umberto .
NANO LETTERS, 2020, 20 (01) :2-10
[8]   SODIUM INTERCALATION INTO THE DEFECT GARNETS FE2(MOO4)3 AND FE2(WO4)3 [J].
BRUCE, PG ;
MILN, G .
JOURNAL OF SOLID STATE CHEMISTRY, 1990, 89 (01) :162-166
[9]   A Critical Review of Machine Learning of Energy Materials [J].
Chen, Chi ;
Zuo, Yunxing ;
Ye, Weike ;
Li, Xiangguo ;
Deng, Zhi ;
Ong, Shyue Ping .
ADVANCED ENERGY MATERIALS, 2020, 10 (08)
[10]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794