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.

Evaluation of performance of machine learning methods in mining structure-property data of halide perovskite materials

With the rapid development of artificial intelligence and machine learning(ML)methods,materials science is rapidly entering the era of data-driven materials informatics.ML models serve as the most crucial component,closely bridging material structure and material properties.There is a considerable difference in the prediction performance of different ML methods for material systems.Herein,we evaluated three categories(linear,kernel,and nonlinear methods)of models,with twelve ML algorithms commonly used in the materials field.In addition,halide perovskite was chosen as an example to evaluate the fitting performance of different models.We constructed a total dataset of 540 halide perovskites and 72 features,with formation energy and bandgap as target properties.We found that different categories of ML models show similar trends for different target properties.Among them,the difference between the models is enormous for the formation energy,with the coefficient of determination(R2)range 0.69-0.953.The fitting performance between the models is closer for bandgap,with the R^(2)range 0.941-0.997.The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap.It shows that the nonlinear-ensemble model,constructed by combining multiple weak learners,effectively describes the nonlinear relationship between material features and target property.In addition,the extreme gradient boosting decision tree model shows the most superior results among all the models and searches for two new descriptors that are crucial for formation energy and bandgap.Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems.

Temperature-induced phase transition of two-dimensional semiconductor GaTe

GaTe is a two-dimensional Ⅲ-Ⅵ semiconductor with suitable direct bandgap of~1.65 eV and high photoresponsivity,which makes it a promising candidate for optoelectronic applications.GaTe exists in two crystalline phases:monoclinic(m-GaTe,with space group C2/m) and hexagonal(h-GaTe,with space group P63/mmc).The phase transition between the two phases was reported under temperature-varying conditions,such as annealing,laser irradiation,etc.The explicit phase transition temperature and energy barrier during the temperature-induced phase transition have not been explored.In this work,we present a comprehensive study of the phase transition process by using first-principles energetic and phonon calculations within the quasi-harmonic approximation framework.We predicted that the phase transition from h-GaTe to m-GaTe occurs at the temperature decreasing to 261 K.This is in qualitative agreement with the experimental observations.It is a two-step transition process with energy barriers 199 meV and 288 meV,respectively.The relatively high energy barriers demonstrate the irreversible nature of the phase transition.The electronic and phonon properties of the two phases were further investigated by comparison with available experimental and theoretical results.Our results provide insightful understanding on the process of temperature-induced phase transition of GaTe.

Exploration of B-site alloying in partially reducing Pb toxicity and regulating thermodynamic stability and electronic properties of halide perovskites

Alloying strategies provide a high degree of freedom for reducing lead toxicity,improving thermodynamic stability, tuning the optoelectronic properties of ABX3 halide perovskites by varying the alloying element species and their contents.Given the key role of B-site cations in contributing band edge states and modulating structure factors in halide perovskites,the partial replacement of Pb2+with different B-site metal ions has been proposed.Although several experimental attempts have been made to date,the effect of B-site alloying on the stability and electronic properties of halide perovskites has not been fully explored.Herein,we take cubic CsPbBr3 perovskite as the prototype material and systematically explore the effects of B-site alloying on Pb-containing perovskites.According to the presence or absence of the corresponding perovskite phase,the ten alloying elements investigated are classified into three types(i.e.,Type Ⅰ:Sn Ge,Ca,Sr;Type Ⅱ:Cd,Mg,Mn;Type Ⅲ:Ba,Zn,Cu).Based on the first-principles calculations,we obtain the following conclusions.First,these B-site alloys will exist as disordered solid solutions rather than ordered structures at room temperature throughout the composition space.Second,the alloying of Sn and Ge enhances the thermodynamic stability of the cubic perovskite host,whereas the alloying of the other elements has no remarkable effect on the thermodynamic stability of the cubic perovskite host.Third,the underlying physical mechanism for bandgap tuning can be attributed to the atomic orbital energy mismatch or quantum confinement effect.Fourth,the alloying of different elements demonstrates the diversity in the regulation of crystal structure and electronic properties,indicating potential applications in photovoltaic s and self-trapped exciton-based light-emitting applications.Our work provides theoretical guidance for using alloying strategies to reduce lead toxicity,enhance stability,and optimize the electronic properties of halide perovskites to meet the needs of optoelectronic applications.

Gut microbiota research nexus:One Health relationshipbetween human,animal,and environmental resistomes

The emergence and rapid spread of antimicrobial resistance is of global public health concern.The gut microbiota harboring diverse commensal and opportunistic bacteria that can acquire resistance via horizontal and vertical gene transfers is considered an important reservoir and sink of antibiotic resistance genes(ARGs).In this review,we describe the reservoirs of gut ARGs and their dynamics in both animals and humans,use the One Health perspective to track the transmission of ARG-containing bacteria between humans,animals,and the environment,and assess the impact of antimicrobial resistance on human health and socioeconomic development.The gut resistome can evolve in an environment subject to various selective pressures,including antibiotic administration and environmental and lifestyle factors(e.g.,diet,age,gender,and living conditions),and interventions through probiotics.Strategies to reduce the abundance of clinically relevant antibiotic-resistant bacteria and their resistance determinants in various environmental niches are needed to ensure the mitigation of acquired antibiotic resistance.With the help of effective measures taken at the national,local,personal,and intestinal management,it will also result in preventing or minimizing the spread of infectious diseases.This review aims to improve our understanding of the correlations between intestinal microbiota and antimicrobial resistance and provide a basis for the development of management strategies to mitigate the antimicrobial resistance crisis.

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.

Anisotropic phonon thermal transport in two-dimensional layered materials

Two-dimensional layered materials(2DLMs)have attracted growing attention in optoelectronic devices due to their intriguing anisotropic physical properties.Different members of 2DLMs exhibit unique anisotropic electrical,optical,and thermal properties,fundamentally related to their crystal structure.Among them,directional heat transfer plays a vital role in the thermal management of electronic devices.Here,we use density functional theory calculations to investigate the thermal transport properties of representative layered materials:β-InSe,γ-InSe,MoS2,and h-BN.We found that the lattice thermal conductivities ofβ-InSe,γ-InSe,MoS_(2),and h-BN display diverse anisotropic behaviors with anisotropy ratios of 10.4,9.4,64.9,and 107.7,respectively.The analysis of the phonon modes further indicates that the phonon group velocity is responsible for the anisotropy of thermal transport.Furthermore,the low lattice thermal conductivity of the layered InSe mainly comes from low phonon group velocity and atomic masses.Our findings provide a fundamental physical understanding of the anisotropic thermal transport in layered materials.We hope this study could inspire the advancement of 2DLMs thermal management applications in next-generation integrated electronic and optoelectronic devices.

Spontaneous low-temperature crystallization of α-FAPbI3 for highly efficient perovskite solar cells

Formamidinium lead triiodide(HC(NH2)2PbI3 or FAPbI3)is a promising light absorber for high-efficiency perovskite solar cells because of its superior light absorption range and thermal stability to CH3NH3PbI3(MAPbI3).Unfortunately,it is difficult to fabricate high-quality FAPbI3 thin films to surpass the MAPbI3-based cells due to easily forming unwanted but more stable yellow d-phase and thus requiring high annealing-temperature for wanted photovoltaic-active black a-phase.Herein,we reported a novel low-temperature fabrication of highly crystallized a-FAPbI3 film exhibiting uniaxial-oriented nature with large grain sizes up to 2 lm.First-principles energetic calculations predicted that this novel deposition should be ascribed to the formation of a high-energy metastable two-dimensional(2D)intermediate of MAFAPbI3 Cl followed by a spontaneous conversion to a-FAPbI3.The ions exchange reaction during this MAFAPbI3 Cl-FAPbI3 conversion account for the perovskite film uniaxial-oriented grown along the(111)direction.This large-grain and uniaxial-oriented grown a-FAPbI3 based solar cells exhibited an efficiency up to 20.4%accompanying with low density-voltage(J-V)hysteresis and high stability.

JAMIP:面向材料基因工程研究的功能材料设计开源软件

材料信息学或材料基因工程作为新兴的材料研究与设计范式,通过深度结合材料大数据与人工智能机器学习算法,正在加速新材料、新功能和新原则的创新发现.如何高效产生、收集、管理、学习和挖掘大规模材料数据是开展材料信息学或材料基因工程研究的关键.JAMIP(Jilin Artificial-intelligence-aided Materials-design Integrated Package)材料设计软件为满足这方面的研究需求而设计,涵盖半导体材料、介电材料、金属材料等材料体系,为基于功能材料大数据与机器学习算法结合的新材料发现和设计提供工具支撑.软件基于Python语言开发,代码开源,既可以基于结构原型数据库高效开展大规模高通量材料计算,也可以实现对计算任务更精细的控制及新任务流程的灵活定制.软件包主体框架包含以高通量材料计算为核心的数据产生、数据收集、管理工具及数据存储、机器学习/数据挖掘等功能模块.机器学习模块集成了数据预处理、数据特征工程,以及常用机器学习算法的模型构建和性能评估子模块.软件各模块之间高度融合,能够高效产生、分析、管理和学习计算材料大数据,为开展材料信息学或材料基因工程研究、实现新材料设计提供专业化的操作软件平台.

Band structure engineering through van der Waals heterostructing superlattices of two-dimensional transition metal dichalcogenides

The indirect-to-direct band-gap transition in transition metal dichalcogenides(TMDCs)from bulk to monolayer,accompanying with other unique properties of two-dimensional materials,has endowed them great potential in optoelectronic devices.The easy transferability and feasible epitaxial growth pave a promising way to further tune the optical properties by constructing van der Waals heterostructures.Here,we performed a systematic high-throughput first-principles study of electronic structure and optical properties of the layerby-layer stacking TMDCs heterostructing superlattices,with the configuration space of[(MX2)n(M0X02)10−n](M/M0=Cr,Mo,W;X/X0=S,Se,Te;n=0-10).Our calculations involving long-range dispersive interaction show that the indirect-to-direct band-gap transition or even semiconductor-to-metal transition can be realized by changing component compositions of superlattices.Further analysis indicates that the indirect-to-direct band-gap transition can be ascribed to the in-plane strain induced by lattice mismatch.The semiconductor-to-metal transition may be attributed to the band offset among different components that is modified by the in-plane strain.The superlattices with direct band-gap show quite weak band-gap optical transition because of the spacial separation of the electronic states involved.In general,the layers stacking-order of superlattices results in a small up to 0.2 eV band gap fluctuation because of the built-in potential.Our results provide useful guidance for engineering band structure and optical properties in TMDCs heterostructing superlattices.

Molecular Scaffold Growth of Two-Dimensional, Strong Interlayer-Bonding-Layered Materials

Currently,most two-dimensional(2D)materials that are of interest to emergent applications have focused on van der Waals–layered materials(VLMs)because of the ease with which the layers can be separated(e.g.,graphene).Strong interlayer-bonding-layered materials(SLMs)in general have not been thoroughly explored,and one of the most critical present issues is the huge challenge of their preparation,although their physicochemical proper-ty transformation should be richer than VLMs and deserves greater attention.MAX phases are a classi-cal kind of SLM.