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

Open framework structures(e.g.,ScF_(3),Sc_(2)W_(3O)_(12),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.

High-throughput computational material screening of the cycloalkane-based two-dimensional Dion–Jacobson halide perovskites for optoelectronics

Two-dimensional(2D) layered perovskites have emerged as potential alternates to traditional three-dimensional(3D)analogs to solve the stability issue of perovskite solar cells. In recent years, many efforts have been spent on manipulating the interlayer organic spacing cation to improve the photovoltaic properties of Dion–Jacobson(DJ) perovskites. In this work, a serious of cycloalkane(CA) molecules were selected as the organic spacing cation in 2D DJ perovskites, which can widely manipulate the optoelectronic properties of the DJ perovskites. The underlying relationship between the CA interlayer molecules and the crystal structures, thermodynamic stabilities, and electronic properties of 58 DJ perovskites has been investigated by using automatic high-throughput workflow cooperated with density-functional(DFT) calculations.We found that these CA-based DJ perovskites are all thermodynamic stable. The sizes of the cycloalkane molecules can influence the degree of inorganic framework distortion and further tune the bandgaps with a wide range of 0.9–2.1 eV.These findings indicate the cycloalkane molecules are suitable as spacing cation in 2D DJ perovskites and provide a useful guidance in designing novel 2D DJ perovskites for optoelectronic applications.

Alloy-induced reduction and anisotropy change of lattice thermal conductivity in Ruddlesden–Popper phase halide perovskites

The effective modulation of the thermal conductivity of halide perovskites is of great importance in optimizing their optoelectronic device performance.Based on first-principles lattice dynamics calculations,we found that alloying at the B and X sites can significantly modulate the thermal transport properties of 2D Ruddlesden−Popper(RP)phase halide perovskites,achieving a range of lattice thermal conductivity values from the lowest(κ_(c)=0.05 W·m^(−1)·K^(−1)@Cs_(4)AgBiI_(8))to the highest(κ_(a/b)=0.95 W·m^(−1)·K^(−1)@Cs4NaBiCl_(4)I_(4)).Compared with the pure RP-phase halide perovskites and three-dimensional halide perovskite alloys,the two-dimensional halide perovskite introduces more phonon branches through alloying,resulting in stronger phonon branch coupling,which effectively scatters phonons and reduces thermal conductivity.Alloying can also dramatically regulate the thermal transport anisotropy of RP-phase halide perovskites,with the anisotropy ratio ranging from 1.22 to 4.13.Subsequently,analysis of the phonon transport modes in these structures revealed that the lower phonon velocity and shorter phonon lifetime were the main reasons for their low thermal conductivity.This work further reduces the lattice thermal conductivity of 2D pure RP-phase halide perovskites by alloying methods and provides a strong support for theoretical guidance by gaining insight into the interesting phonon transport phenomena in these compounds.

Computational functionality-driven design of semiconductors for optoelectronic applications

The rapid development of the semiconductor industry has motivated researchers passion for accelerating the discovery of advanced optoelectronic materials.Computational functionality-driven design is an emerging branch of material science that has become effective at making material predictions.By combining advanced solid-state knowledge and high-throughput firstprinciples computational approaches with intelligent algorithms plus database development,experts can now efficiently explore many novel materials by taking advantage of the power of supercomputer architectures.Here,we discuss a set of typical design strategies that can be used to accelerate inorganic optoelectronic materials discovery from computer simulations:In silico computational screening;knowledge-based inverse design;and algorithm-based searching.A few representative examples in optoelectronic materials design are discussed to illustrate these computational functionality-driven modalities.Challenges and prospects for the computational functionality-driven design of materials are further highlighted at the end of the review.