Yttrium barium copper oxide superconducting transition temperature modeling through gaussian process regression

Xu²

2020

ChemistrySelect

Self Cite

Cubic perovskites have attracted great attention in the past decade due to unique and tunable optical, mechanical, and electrical properties, which are promising candidates for various applications such as solar cells, light emitting diodes, actuators, and laser cooling devices. The lattice constant, a, as the only variable parameter among the six parameters in the crystal structure, has a significant impact on the structural stability, bandgap structure, and thus materials performance. In this study, we develop the Gaussian process regression (GPR) model to shed light on the statistical relationship between ionic radii and lattice constants for cubic perovskite ABX3 compounds. A total of 135 samples with lattice constants ranging from 3.680 Å to 6.330 Å are explored. The model has a high degree of accuracy and stability that contributes to fast and robust lattice constant estimations.

Section: Introductionmentioning

confidence: 99%

Machine Learning Lattice Constants for Cubic Perovskite Compounds

Xu²

2020

ChemistrySelect

Self Cite

“…The introduction of the methodology follows previous studies 36‐59 . GPRs are nonparametric probabilistic models.…”

Section: Methodsmentioning

confidence: 99%

“…The model is highly stable and accurate that contributes to efficient and low‐cost lattice constant estimations and understandings of which based on ionic radii and electronegativities of both ternary and mixed pyrochlores. As a machine learning technique, 34,35 the GPR model has been used various areas of materials science and engineering to acquire important physicochemical parameters through rapid and robust predictions 36‐59 . This model could serve as a guideline for pyrochlore lattice design and might be adopted for lattice mismatch estimations in thin film configurations.…”

Section: Introductionmentioning

confidence: 99%

Machine learning the lattice constant of cubic pyrochlore compounds

Int J Applied Ceramic Tech

2021

Self Cite

Pyrochlores, with a general formula A 2 B 2 X 7 , are promising candidates in many potential applications due to their wide varieties of physical properties. Different combinations of cations and anions in the crystal structure enable the tailoring of their properties and functionalities. For cubic pyrochlores, the lattice constant, a, is an integrated result of stoichiometry, ionic radii, and electronegativities of alloying elements.It also has significant impacts on stabilities, electronic structures, ionic conductivities, and thus performance of materials. Here, we develop the Gaussian process regression model to shed light on the relationship among ionic radii, electronegativities, and lattice constants for cubic pyrochlores. The dataset includes ternary pyrochlores and mixed pyrochlores with co-doping and multi-doping situations. One hundred and thirty-nine samples with lattice constants between 9.287 and 11.150 Å are examined.The model is highly stable and accurate that contributes to efficient and low-cost lattice constant estimations.

“…As one of the computational intelligence techniques, the GPR model has been utilized in other materials systems to predict significant physical parameters in different fields of applications. [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] This model can serve as a guideline for monoclinic double perovskite design and can be used as part of machine learning to aid the understanding of relationships between ion sizes and lattice constants. be the mean function, and consider the GPR model y = b(x) T β + l(x), where l(x)$GP(0, k(x, x 0 )) and b(x) R p .…”

mentioning

confidence: 99%

“…Detailed kernel and basis function specifications can be found in previous studies. [35][36][37][38][39][40][41][42][43][44][45][46][47][49][50][51][52] For model parameter estimations, cross validation and Bayesian optimizations are used. For the former, 10 randomized folds are utilized (see Tables 1-3), and for the latter, the expected improvement per second plus (EIPSP) algorithm is adopted.…”

mentioning

confidence: 99%

Machine learning lattice parameters of monoclinic double perovskites

Int J of Quantum Chemistry

2020

Self Cite

Monoclinic double perovskites, such as A2B′B″O6, exhibit a wide range of unique electronic, magnetic, and optical properties, depending on the alloying elements, ordering, and lattice distortion. Four lattice constants, a, b, c, and β, can vary due to the ionic radii of alloying elements, which represent the compression, expansion, and tilting of octahedra in the crystal structure. In the current study, we develop the Gaussian process regression model to shed light on relationships between effective ionic radii and lattice constants for monoclinic double perovskites. The model is highly accurate and stable, contributing to fast, low‐cost estimations of lattice constants.