Computational Materials Science最新文献

The unexpected performance degradation often occurs when ion beams are applied to improve graphene devices performance and the mechanism of performance degradation is still controversial. The current theoretical research on the degradation mechanism nearly overlooks the influence of the substrate. In this work, the low-energy ion irradiation response of the Graphene/α-SiO₂ system is investigated by molecular dynamics and first-principles calculations to understand the possible impact of the substrate. The 40 eV∼10 keV C ions are selected as self-ions for irradiation to avoid the introduction of impurities. The simulated results show that some low-energy C ions rebound between the graphene layer and α-SiO₂ substrate because some of the C ions are rebounded on the substrate surface rather than entering the substrate. The flat graphene becomes a ripple structure due to the rebound of C ions and the distance between graphene and substrate increases. The ripples result in the indirect band gap and the increased effective mass to degrade the electronic performance of graphene devices. In addition, the coupling between ripples and vacancy defects significantly exacerbates the degradation of graphene transport capacity. The substrate is still amorphous during irradiation, but some C ions entered the substrate hinder its insulation property. Overall, the changes in electronic properties caused by ripple structures coupled with vacancy defects should be an important factor responsible for device performance degradation. This work provides a new insight into the performance modification and degradation mechanism of graphene-based devices by ion beams.

In this study, we aim to comprehensively investigate the effects of surface passivation and layer thickness modulation on the structural and optoelectronic properties of 2D β-Ga₂O₃ using first-principles calculations. Our bonding character simulations predict the formation of fully hydrogenated, fully halogenated, and hydro-halogenated monolayers of β-Ga₂O₃. The results show that hydrogenation, fluorination, hydro-fluorination, and hydro-chlorination effectively passivate monolayer β-Ga₂O₃, whereas chlorination, bromination, iodization, hydro-bromination, and hydro-iodization do not. The failure of these latter processes is attributed to the large atomic radii of the passivating atoms, which induce significant lattice distortions. The electronic properties, including band gap and band edge level, are primarily influenced by the electronegativities and orbital energies of the passivating atoms. For pristine 2D β-Ga₂O₃, electronic properties are largely independent of layer thickness. However, in atom-passivated 2D β-Ga₂O₃, band gaps and electron affinities vary with the number of layers due to enhanced coupling between the passivating atoms and Ga/O, along with relatively minor shifts in the conduction band minimum. Additionally, both atom passivation and layer thickness modulation improve various optical properties of 2D β-Ga₂O₃, including dielectric function, optical absorption, and photoconductivity. Notably, the newly reported hydro-chlorination configuration demonstrates lower energy compared to previously reported configurations, along with a direct band gap, an elevated valence band edge, and enhanced optical absorption relative to its bare form. Our study provides theoretical insights into the manipulation of electronic and optical properties in 2D β-Ga₂O₃, establishing a foundation for surface charge transfer doping of β-Ga₂O₃.

Molecular properties play a crucial role in material discovery, protein interaction and drug development. The appearance of Graph Neural Network (GNN) significantly improved the performance of molecular property prediction. However, nodes in GNN only update the features of neighbor nodes, resulting in insufficient ability to encode global feature information. The self- attention mechanism in transformer can encode the global information except for local information of molecules, while its spatial information is insufficient. Since molecules are three-dimensional spatial structures, spatial geometry information is an important attribute for molecules properties. To consider these factors, a network model of Space Graph Neural Network coupled Transformer (SGNN-T) is proposed in this paper which can combine global and local molecule information with three-dimensional spatial structures for molecular properties prediction. In this model, Graph neural network Geometric Feature Fusion Module (GGFF) and Transformer Spatial Geometric Feature Enhancement Module (TSGFE) are included to enhance the spatial geometry learning ability of the network. The GGFF module constructs a parallel graph neural network by thinking over atoms, bonds and bond angles at the same time which effectively complements the spatial information of the network by leading into bond angles than normal GNN. The TSGFE module introduces the coordinates and centrality degree features coupled with the features by GGFF into transformer to further enhance the geometric expression ability of the module. Through these two parts, SGNN-T model can encode local and global information of molecules at the same time. Property prediction experiments are executed on the QM9, OMDB and MEGNet dataset. The results of MAE show the proposed model has the best performance than the popular models.

Structural defects, including mono- and double- vacancies, commonly presented at the surface of two-dimensional materials (2D), including 2D ternary nitrides. These point defects can alter electronic structure of 2D ternary nitrides. In this work, density functional theory based simulations are utilized for a comprehensive characterization of point defects in Zn₂(V,Nb,Ta)N₃ monolayers. The monovacancies of Z and N in Zn₂(V,Nb,Ta)N₃ monolayers are found to have the lowest formation energy among all studied defects. The presence of the monovacancy of N leads to a blue shift of valance and conduction bands of the Zn₂(V,Nb,Ta)N₃ monolayers and the formation of deep trap states in their fundamental gap in the vicinity of the Fermi level, while the presence of the monovacancy of Zn induces the formation of shallow trap states within the fundamental gap on the Zn₂(V,Nb,Ta)N₃ monolayers. The scanning tunneling microscopy simulated images of point defects in Zn₂(V,Nb,Ta)N₃ monolayers obtained in this work can facilitate the detection of these defects in experiments. Therefore, the theoretical characterization of defects in Zn₂(V,Nb,Ta)N₃ monolayers presented in this work can provide helpful guidance for future experiments.

Materials informatics uses machine learning to predict the properties of new materials, but generally requires extensive characterisation and feature extraction to describe the input data, which can be time consuming and expensive. Predicting properties or classes of materials based on minimal input information, such as a chemical formula, can be a useful first step to identify which materials are promising candidates before investing resources. This is particularly desirable when working with complex compounds containing a large variety of elements, such as materials for battery applications. In this paper we show how to classify battery compounds into either charge or discharge formulas, or identify suitable anode or cathode materials, based exclusively on the chemical formulas of materials available in online repositories. Without any structural information, we train high-performing classifiers that can be used to rapidly screen hypothetical materials and assign potential applications. The models are applied to a total of 471 materials from the literature, and deliver a 96% success rate over 80% probability. These methods are general and the workflow can be applied to any complex crystalline materials to predict end-uses in advance of synthesis or simulation, opening up the opportunity for machine learning to use used for research planning, in addition to prediction or inference.

Monolayer magnesium diboride (MgB${}_2$), a novel 2D material, has garnered significant interest due to its unique physical properties. This paper studies theoretically the electronic band structures, phonon dispersions and optical properties of the MgB${}_2$ monolayer under in-plane biaxial strain using first-principles calculations. The results show that the electronic states and dielectric functions are significantly modulated by strain, suggesting it is an effective way to achieve the target electronic and optical properties. At the same time, based on the phonon analysis, we proved that the system remains dynamically stable in the range of $-2\text{\%}$ to 5% biaxial strain. Moreover, our results show that the MgB${}_2$ monolayer exhibits high transmissivity in the visible region due to its low absorption and reflectivity, making it an excellent candidate for optoelectronic applications such as transparent electrodes. On the other hand, the high absorption and reflectivity in the UV region indicate that it absorbs light most effectively in the ultraviolet spectrum. This characteristic demonstrates its suitability for applications requiring UV absorption, detection, and protection, such as UV filters and photodetectors.

Because of remarkable irradiation resistance and mechanical properties, high entropy alloys (HEAs) are expected to be a candidate structural material in the next-generation nuclear power plants. Besides, the nano-crystalline (NC) materials also exhibit excellent radiation tolerance and good thermal stability. The NC-HEAs may have superior irradiation resistance than both NC and HEA materials. However, how the irradiation-resistant effects of HEAs and grain boundary (GB) jointly affect the irradiation damage evolution in NC HEAs is an interesting but rarely reported topic. Considering these, the present work investigated the irradiation defect production and accumulation characteristics in NC-HEA FeCoCrNi and NC-Ni by cascade overlapping simulations. The evolution of irradiation clusters within NC grains was quantitatively analyzed for the first time using a newly developed method. The results show that the irradiation defects produced in NC-HEA are more diminutive and uniformly distributed than those in NC-Ni with a similar irradiation dose despite the total number of survived point defects being very close in the two cases. Further investigation shows that such a better irradiation resistance of NC-HEA can be attributed to the synergy between the severe lattice distortion effect in HEAs and the interstitial sink effect of GBs in NC. The present study may provide some fundamental understanding of the irradiation defect evolution mechanisms for the novel HEAs that potentially serve as structural materials in advanced reactors.

Two-dimensional transition metal oxides and MXenes stand out as potential anode materials for alkali metal ion batteries, yet their performance is hindered by semiconductor conductivity and layer re-stacking issues. To circumvent these limitations, we explored the use of van der Waals heterostructures, specifically assembling a graphene-like ZnO/Ti₂CS₂ heterostructure (g-ZnO/Ti₂CS₂), and evaluated its efficacy as an anode material for Li/Na/K ion batteries (LIBs/NIBs/KIBs) through first-principles calculations. Our findings reveal that g-ZnO/Ti₂CS₂ maintains thermal stability at room temperature and demonstrates metallic conductivity. It also supports stable adsorption of single Li/Na/K atoms and facilitates their diffusion with a barrier under 0.5 eV, indicating superior rate performance. Furthermore, g-ZnO/Ti₂CS₂ exhibits an average open circuit voltage (OCV) between 0–1 V and delivers specific capacities of 529/317/317 mAh/g for LIBs/NIBs/KIBs, surpassing traditional graphite anodes. These characteristics indicate that g-ZnO/Ti₂CS₂ is a promising anode material, particularly for LIBs, offering a theoretical foundation for future anode material research for alkali metal ion batteries.

The differences between nanoparticles and nanovoids cannot be clearly distinguished energetically using conventional comparisons based on the surface energies of these species. For example, nanoparticles and nanovoids with the same volume and shape are considered energetically equivalent to the conventional Wulff construction, and so the difference in their morphology cannot be evaluated. This can be attributed to fact that using such approaches, the effects of excess defects, edges, and vertices in nanoparticles and nanovoids are typically ignored. In this study, we investigated the energetic differences between face-centered-cubic (FCC) nanoparticles of Al and nanovoids in bulk FCC Al structure with conventional truncated octahedral shapes by calculating the excess energies attributed to their edges, vertices, and sizes. This was achieved using density functional theory calculations and our previously reported method for evaluating the effects of edges and vertices. The morphological differences between the nanoparticles and nanovoids were also discussed based on the obtained results.

In the present article, Monte Carlo simulations were used to investigate the magnetic properties and magnetocaloric effetc of Polyphenylene Dendrimers bilayers under the Ruderman-Kittel-Kasuya-Yosida exchange interactions, considering mixed spins S = 5/2 and σ = 2. The phases of the ground state at zero temperature are explained in more detail. Additionally, we analyze the effects of varying the number of non-magnetic layers (NML), reduced exchange interactions between the two first nearest neighbors of spins S-S (R_SS), between spins σ-σ (R_σσ), and reduced crystal field (d) on the total magnetization and hysteresis loops. Besides, the parameters NML, R_SS, R_σσ, and d were varied to determine the reduced critical temperature. The impact of different reduced external magnetic fields (h/J_Sσ) on specific heat, magnetic entropy changes, and adiabatic temperature changes was studied as functions of reduced temperature. Finally, we have examined the relative cooling power (RCP) for several parameters h/J_Sσ.