Computational Materials Science最新文献

{"title":"Molecular dynamics modelling of the stress–strain response of β-sheet nanocrystals","authors":"","doi":"10.1016/j.commatsci.2024.113367","DOIUrl":"10.1016/j.commatsci.2024.113367","url":null,"abstract":"<div><p>Molecular dynamics simulations were conducted on two model antiparallel β-sheet crystallites [GA]n and [GAS]n to study deformation in chain, sheet stacking, and hydrogen bonding directions under uniaxial loading. In chain direction, both models were mechanically stable, even beyond the 570 K amorphousation temperature of silk fiber; however, [GA]n model displayed higher yield strain, stress, elastic modulus, and resilience than [GAS]n. In transverse directions, they had similar stress–strain behavior and demonstrated significant anisotropic mechanical behavior. Hence, inclusion of an amino acid with a rich side chain group extending between β-sheets reduces the stiffness of crystallite in chain direction. Serine and alanine residues maintained existing H-bonds and established new ones during stretching in chain direction and shrinking in transverse directions which affected the mechanical response near the yield point. Comparison between β-sheet crystallite and PPTA (Kevlar) showed that the mechanical performance of these crystal polymers were very similar in chain direction, but contrarily β-sheet crystallite had higher stiffness in H-bonding and sheet stacking directions than PPTA. This study may provide a guideline in designing of polyaminoacid based biocompatible materials with superior mechanical performance.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142229992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interfacial thermal resistance in stanene/ hexagonal boron nitride van der Waals heterostructures: A molecular dynamics study","authors":"","doi":"10.1016/j.commatsci.2024.113359","DOIUrl":"10.1016/j.commatsci.2024.113359","url":null,"abstract":"<div><p>Recently, the stanene (Sn)/hexagonal boron nitride (h-BN) van der Waals heterostructure (vdW) has garnered significant attention among the scientific community due to its distinctive electrical, optical, and thermal characteristics. Despite the promising potential of this heterostructure, the interfacial thermal resistance (ITR) between the Sn and h-BN layers remains unexplored. Understanding and modulating this ITR are essential steps towards harnessing the maximum potential of these materials in practical nanodevices. This study aims to investigate the interfacial thermal resistance (ITR) between the Sn and h-BN layers through the use of conventional molecular dynamics (MD) simulation. The transient pump–probe heating technique, commonly referred to as the Fast Pump Probe (FPP) approach, is utilized to analyze the ITR of the Sn/h-BN heterostructure. The estimated ITR value of a 30 × 10 nm² Sn/h-BN nanosheet is found to be around ∼ 7 × 10^-8 K.m²/W at room temperature. This study comprehensively investigates the impact of various internal and external parameters including nanosheet size, system temperature, contact pressure, vacancy concentration, and mechanical tensile strain (uniaxial and biaxial) on ITR, providing an extensive understanding of how these factors collectively affect the thermal resistance between Sn and h-BN layers. The simulation<!--> <!-->results demonstrate a consistent decline in ITR by approximately ∼ 93 %, ∼45 %, ∼65 %, and ∼ 33 % with the increasing system size, temperature, contact pressure, and defect concentration, respectively. In contrast, increasing mechanical strain leads to a substantial enhancement in ITR, with a maximum increase of approximately ∼ 47 % under uniaxial tensile strain and almost ∼ 99 % under biaxial tensile strain. Moreover, the pristine Sn/h-BN heterostructure exhibits no significant thermal rectification effect. The Phonon Density of States (PDOS) profile of the Sn and h-BN layer is calculated to elucidate this underlying behavior of ITR. The PDOS analysis reveals that heat is transported from h-BN to the Sn layer through efficient coupling of low-frequency flexural phonons between these two materials. This work will provide both theoretical support and logical guidelines for modulating thermal resistance across diverse dissimilar material interfaces, which will be necessary for the development of advanced nanodevices used in next-generation nanoelectronics, nanophotonic, and optoelectronics applications.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142229925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phase-field modeling of interdiffusion between dissimilar Fe-Cr-Ni alloys during non-isothermal hot isostatic pressing","authors":"","doi":"10.1016/j.commatsci.2024.113357","DOIUrl":"10.1016/j.commatsci.2024.113357","url":null,"abstract":"<div><p>Powder metallurgy hot isostatic pressing (PM-HIP) has emerged as a promising alternative to welding for joining dissimilar metals. During HIP, interfacial bonding is mediated by solid state diffusion. The interdiffusion zone across the interface depends on processing conditions, calling for the need for accurate numerical tools capable of simulating interdiffusion and possible phase transformation in order to optimize processing parameters. Here, a phase-field (PF) model based on CALPHAD-based free energy functionals is developed to simulate the interdiffusion and phase evolution between dissimilar Fe–Cr–Ni based steels undergoing HIP and is demonstrated using the interface between 316L and SA508 steels. To overcome the numerical challenges caused by the singular magnetic and entropy terms in the CALPHAD free energy models in the Fe–Cr-Ni system, polynomial functions are fitted with temperature dependent coefficients represented by Fourier series to accurately describe the phase stability of both fcc and bcc phases in the composition and temperature space. This enables simulations of non-isothermal HIP cycles. Diffusivity data from commercial software and literature are taken to parameterize the kinetic parameters. A discrete nucleation model is incorporated for possible phase transformation. The modified thermodynamic models are validated against previous experiments at 923 K and 1273 K. The interdiffusion kinetics are benchmarked against new HIP experiments joining powder and bulk 316L to bulk SA508 with three different HIP cycles. The good agreement between simulations and experiments on both phase stability and interdiffusion indicate that the model is suitable for simulating interdiffusion between Fe–Cr–Ni alloys during HIP cycles. It is also found that using powder and bulk 316L gives similar interdiffusion profiles at elevated temperature when a dense interface forms during HIP.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of point defects on charge transport in nickel ferrite NiFe2O4","authors":"","doi":"10.1016/j.commatsci.2024.113326","DOIUrl":"10.1016/j.commatsci.2024.113326","url":null,"abstract":"<div><p>The paper considers electronic structure of pristine and defective nickel ferrite (spinel <span><math><mrow><mtext>Ni</mtext><msub><mrow><mtext>Fe</mtext></mrow><mrow><mn>2</mn></mrow></msub><msub><mrow><mtext>O</mtext></mrow><mrow><mn>4</mn></mrow></msub></mrow></math></span>). The orbital ordering, band gap and charge transfer are studied in the framework of density functional theory with account of strong electronic correlations (DFT+U method). The possibility of changing the type of polaron transport in the presence of oxygen vacancies and nickel antisites has been demonstrated. The corresponding non-adiabatic activation barriers of polaron transport is considered. The resulting hopping energies are in general agreement with experimentally observed activation energies. The highlighted influence of point defects on the polaron conductivity mechanism could be a suitable explanation for the large variability of activation energies in previous experimental works. NEGF-DFT calculations were also performed to consider a possible band conduction mechanism. The enhanced conduction with the presence of oxygen bi-vacancies, and a change in carrier type is also observed.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting actuation strain in quaternary shape memory alloy NiTiHfX using machine learning","authors":"","doi":"10.1016/j.commatsci.2024.113345","DOIUrl":"10.1016/j.commatsci.2024.113345","url":null,"abstract":"<div><p>Data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Considering most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX actuation strain prediction.</p><p>To forecast the NiTiHfX AS, a total of 901 data sets or 17,119 data points for eighteen inputs and one output were gathered, verified, and selected. Several machine-learning approaches were applied and joined to gather to guarantee robust modeling. The global model’s overall determination factor (R²) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927025624005664/pdfft?md5=232342533694fd5540ee0b92d02bc792&pid=1-s2.0-S0927025624005664-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ripple structure and electronic property degradation of Graphene/α-SiO2 induced by low-Energy self‐Ion irradiation","authors":"","doi":"10.1016/j.commatsci.2024.113347","DOIUrl":"10.1016/j.commatsci.2024.113347","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tuning the optoelectronic properties of two-dimensional β-Ga2O3 using surface passivation and the layer thickness","authors":"","doi":"10.1016/j.commatsci.2024.113346","DOIUrl":"10.1016/j.commatsci.2024.113346","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SGNN-T: Space graph neural network coupled transformer for molecular property prediction","authors":"","doi":"10.1016/j.commatsci.2024.113358","DOIUrl":"10.1016/j.commatsci.2024.113358","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Density functional theory based characterization of point defects in two-dimensional Zn2(V,Nb,Ta)N3 ternary nitrides","authors":"","doi":"10.1016/j.commatsci.2024.113356","DOIUrl":"10.1016/j.commatsci.2024.113356","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting battery applications for complex materials based on chemical composition and machine learning","authors":"","doi":"10.1016/j.commatsci.2024.113344","DOIUrl":"10.1016/j.commatsci.2024.113344","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927025624005652/pdfft?md5=9b1255047d1b99f95111da7e16ffd4be&pid=1-s2.0-S0927025624005652-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}