Effect of twin grain boundary on material thermal expansion

Zou

et al. 2022

J. Phys. Chem. Lett.

Self Cite

The considerable thermal expansion of halide perovskites is one of the challenges to device stability, yet the physical origin and modulation strategy remain unclear. Herein, we report first-principles calculations of the thermal properties of halide perovskites at 300 K using oxides as a reference. We found that the large thermal expansion of halide perovskites can mainly be attributed to their low bulk modulus and volumetric heat capacity because of the soft crystal lattice, whereas composition-dependent anharmonicity emerges as the most important factor in determining thermal expansion with the same structure. We discovered that thermal expansion of halide perovskites can be decreased by weakening the B−X bond to promote the octahedral anharmonicity. We further proposed an effective thermal expansion coefficient descriptor of halide perovskites with a Pearson correlation coefficient of nearly −80%. Our findings provide insights into the underlying mechanisms and chemical trends in the thermal expansion behavior of halide perovskites.

mentioning

confidence: 98%

mentioning

confidence: 99%

Physical Mechanism and Chemical Trends in the Thermal Expansion of Inorganic Halide Perovskites

Zou

et al. 2022

J. Phys. Chem. Lett.

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“…The thermal properties calculations were based on the QHA . In the framework of QHA, the total Helmholtz free energy F can be approximately represented as , F ( V , T ) = E 0 ( V ) + F ph ( V , T ) where E 0 is the total energy at constant volume V and F ph is the phonon contributed Helmholtz free energy at volume V and temperature T . By fitting the energy versus volume curve based on the equation of state, minimizing the Helmholtz free energy with respect to cell volume can obtain the equilibrium volumes at different temperatures. − Therefore, the axial CLTE α i ( T ) can be written as α i ( T ) = 1 V ( T ) normald V ( T ) normald T Within the framework of QHA, α i ( T ) can be expressed as α i ( T ) = C V ( T ) γ i QHA V ( T ) B i where C V ( T ), B i ( T ), and γ i QHA are the specific heat, axial bulk modulus, and Grüneisen parameter fitted by QHA, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The thermal properties calculations were based on the QHA. 76 In the framework of QHA, the total Helmholtz free energy F can be approximately represented as 87,88…”

Section: Methodsmentioning

confidence: 99%

Evaluating In-Plane Thermal Expansion of Two-Dimensional Layered Materials via Effective Descriptors

Sun

et al. 2023

J. Phys. Chem. C

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Two-dimensional layered materials show promising applications in miniaturized devices, such as transistors, spintronics, and field emitters. However, substantial thermal management issues, including thermal mismatch and thermal stress, may degrade device performance. To address such challenges, the thermal expansion (TE) anisotropy determined by the structure feature of layered material needs to be well understood. Here, we propose two new descriptors to evaluate the TE behavior of layered materials, namely the axial elastic deviation factor σ i and the axial net thermal stress f i along the ith direction. The former, defined as the normalized elastic element difference of material elastic tensor C and compliance tensor S, can distinguish whether the thermal expansion of a material is driven by phonons (with small σ i ) or elastic property (with large σ i ) with few computational costs. The latter, axial stress (in GPa/K) induced by temperature, shows an accurate determination of the positive or negative thermal expansion along different in-plane directions of layered materials. Based on the analysis of descriptors, we found that PtS2 and PtSe2 are featured with a larger axial elastic deviation factor (>23%). Considering the elastic property, we for the first time report the in-plane negative thermal expansion in PtS2 (−1.2 ppm/K) and PtSe2 (−0.8 ppm/K). Our work provides a unified understanding of TE causes of layered materials via effective descriptors, which can serve as a guideline for high-throughput screening of thermal expansion materials and subsequent device design.

“…Recent studies have begun to investigate the relationship between thermal expansion and structural characteristics. [30][31][32] The concept of average atomic volume (AAV) was proposed to study cyanide NTE systems, where CN ligands bridge polyhedral units. [31] The study suggests a positive correlation between NTE intensity and AAV.…”

Section: Introductionmentioning

confidence: 99%

Evaluating thermal expansion in fluorides and oxides: Machine learning predictions with connectivity descriptors

Cai

et al. 2023

Chinese Phys. B

Open framework structures (e.g., ScF3, Sc2W3O12, etc.) exhibit significant potential for thermal expansion tailoring owing to their high atomic vibrational degrees of freedom and diverse connectivity between polyhedral units, displaying positive/negative thermal expansion (PTE/NTE) coefficients at a certain temperature. Despite the proposal of several physical mechanisms to explain the origin of NTE, an accurate mapping relationship between the structural-compositional properties and thermal expansion behavior is still lacking. This deficiency impedes the rapid evaluation of thermal expansion properties and hinders the design and development of such materials. We developed an algorithm for identifying and characterizing the connection patterns of structural units in open-framework structures and constructed a descriptor set for the thermal expansion properties of this system, which is composed of connectivity and elemental information. Our developed descriptor, aided by machine learning (ML) algorithms, can effectively learn the thermal expansion behavior in small sample datasets collected from literature-reported experimental data (246 samples). The trained model can accurately distinguish the thermal expansion behavior (PTE/NTE), achieving an accuracy of 92%. Additionally, our model predicted six new thermodynamically stable NTE materials, which were validated through first-principles calculations. Our results demonstrate that developing effective descriptors closely related to thermal expansion properties enables ML models to make accurate predictions even on small sample datasets, providing a new perspective for understanding the relationship between connectivity and thermal expansion properties in the open framework structure. The datasets that were used to support these results are available on Science Data Bank, accessible via the link https://doi.org/10.57760/sciencedb.j00113.00100(https://www.scidb.cn/s/buMzeu)