Computational Materials Science最新文献

{"title":"Interpretable MA-island clusters and fingerprints relating bainite microstructures to composition and processing temperature","authors":"Vinod Kumar ,&nbsp;Sharukh Hussain ,&nbsp;Priyanka S. ,&nbsp;P.G. Kubendran Amos","doi":"10.1016/j.commatsci.2024.113492","DOIUrl":"10.1016/j.commatsci.2024.113492","url":null,"abstract":"<div><div>Realising the affect of composition and processing condition on bainite microstructures is often challenging, owing to the intricate distribution of the constituent phases. In this work, scanning electron micrographs of non-isothermally transformed bainite, with martensite-austenite (MA) islands, are analysed to relate the microstructures to the composition and quench-stop temperature. The inadequacy of the MA-islands’ geometric features, namely aspect ratio, polygon area and compactness, in establishing this relation is made evident from Kullback–Leibler (KL) divergence at the outset. Clustering the bainite microstructures, following a combination of feature extraction and dimensionality reduction, further fails to realise the affect of composition and processing temperature. Integrated machine-learning analysis of the individual MA islands, in contrast to the bainite microstructures, yields interpretable clusters with characteristically distinct size and morphology. These five clusters, referred to as fine- and coarse-dendrite, fine- and coarse-polygon and elongated, are exceptionally discernible and can be adopted to describe any MA island. Characterising the bainite microstructures, based on the distribution of the interpretable MA-island clusters, generates <em>fingerprints</em> that sufficiently relates the composition and processing conditions with the microstructures.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113492"},"PeriodicalIF":3.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656989","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":"Performance analysis of doped zigzag graphene nanoribbon-based device for practical electronic applications using first principle approach","authors":"Hammadur Rub Ansari ,&nbsp;M. Nizamuddin ,&nbsp;Samrah Manzoor ,&nbsp;Prabhash Mishra","doi":"10.1016/j.commatsci.2024.113464","DOIUrl":"10.1016/j.commatsci.2024.113464","url":null,"abstract":"<div><div>The ongoing reduction in the size of electronic devices, interconnect delays have emerged as an important constraint in the overall performance of chips. The time it takes for signals to travel over the interconnecting wires is the cause of these delays, which today frequently exceed the intrinsic delays that are present within the integrated circuits (ICs) themselves. The rate at which current chips operate and their overall efficiency are both significantly influenced by this trend. This research investigates the electrical and electronic properties of a device constructed from doped graphene nanoribbons. The device is analyzed under conditions of minimal applied bias or electric field. It features a channel consisting of a minimal unit cell from a zigzag nanoribbon, situated between two zigzag graphene nanoribbons (ZGNR) which function as the electrodes on the left and right sides. The doping process improves the thermodynamic and structural stability of the device, achieving notably low values for total energy, formation energy, and binding energy. By incorporating nitrogen and boron atoms into specific interstitial sites within the ZGNR, the study aims to enhance understanding of the electronic transport mechanisms involving these dopant atoms and the lattice of the ZGNR. This research explores key semiconductor characteristics of the doped ZGNR-based device, such as negative differential resistance (NDR), peak-to-valley ratio (PVR), and rectification ratio (RR), which are crucial for various electronic applications, including switches, logic circuits, memory storage, amplifiers, and negative resistance oscillators. The device demonstrates a peak-to-valley ratio (PVR) of 92 and a rectification ratio (RR) of 149. Additionally, the device exhibits high dielectric energy storage capacity, with a maximum static dielectric constant of 14.7 and substantial low-energy absorption when nitrogen atoms are incorporated into the electrode region. This suggests potential applications in nanoelectronics and as a dielectric energy storage device operating at low applied bias voltages.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113464"},"PeriodicalIF":3.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657110","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":"Insights into the soft brittle-to-ductile transition from discrete dislocation dynamics","authors":"Hunter K. Brumblay ,&nbsp;Gregory B. Thompson ,&nbsp;Christopher R. Weinberger","doi":"10.1016/j.commatsci.2024.113503","DOIUrl":"10.1016/j.commatsci.2024.113503","url":null,"abstract":"<div><div>The Brittle-to-ductile transition (BDT) in body centered cubic metals exhibits a soft transition wherein the fracture toughness gradually rises to before the onset of ductility. The resultant brittle-to-ductile transition temperature can be described with an Arrhenius relationship whose activation energy is related to plasticity in the material. To provide further insight into the nature of the BDTT, in this work we utilized a discrete dislocation dynamics model with a crack to simulate the BDT and how it depends on the thermally activated nature of plasticity. The interrelationship between the BDT activation energy and the dislocation mobility parameters were determined via the calculation of first order sensitivity coefficients. This analysis allows us to demonstrate that the activation energy for the BDT is directly related to the activation energy for plasticity through an effective stress that defines this relationship. This effective stress physically is the average stress on the dislocations that move out of the crack. Finally, we are able to show that this effective stress is dictated by the low temperature fracture toughness or cleave energy of the material and the source position, the latter of which can be affected by processing. Collectively, these results provide new insight into what controls the thermal activation of the BDT and what are the important parameters to control it.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113503"},"PeriodicalIF":3.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656988","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":"Molecular dynamics simulation of excess vacancy formation during rapid solidification of pure metals","authors":"Tomoya Yazawa ,&nbsp;Yasushi Shibuta ,&nbsp;Munekazu Ohno","doi":"10.1016/j.commatsci.2024.113510","DOIUrl":"10.1016/j.commatsci.2024.113510","url":null,"abstract":"<div><div>The formation of excess vacancies in the solid during isothermal rapid solidification, i.e., vacancy trapping in pure Cu, Fe, Ta, and V was investigated using molecular dynamics simulations. A comparison of the present results with previously reported results revealed that the temperature dependence of nonequilibrium vacancy concentration differs between face-centered cubic (fcc) and body-centered cubic (bcc) metals. In fcc metals, the nonequilibrium vacancy concentration increases monotonically as temperature decreases. In contrast, for bcc metals, the vacancy concentration initially increases and then decreases as the temperature drops. This difference in temperature dependence between fcc and bcc metals is closely associated with the density profile near the solid–liquid interface.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113510"},"PeriodicalIF":3.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657117","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":"Demagnetization field simulation in hexagonal magnetic materials via Fast Fourier Transform","authors":"Teng Yang ,&nbsp;Qing Tang ,&nbsp;Tao Lin ,&nbsp;Jian Han ,&nbsp;Ben Xu","doi":"10.1016/j.commatsci.2024.113497","DOIUrl":"10.1016/j.commatsci.2024.113497","url":null,"abstract":"<div><div>Two-dimensional van der Waals (vdW) magnetic materials with hexagonal structures exhibit exceptional potential for spintronics applications due to their unique magnetic properties. However, the accurate atomistic simulation of the demagnetization field in large hexagonal systems remains a challenge, as traditional Fast Fourier Transform (FFT) methods are limited to orthogonal lattices. In this study, we present a novel preprocessing method for hexagonal lattices to allow efficient computation of long-range demagnetization field by FFT acceleration. Through Atomistic Spin Dynamics simulations on layered CrI<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, we reveal the significant impact of the demagnetization field on non-collinear spin textures, including the formation of Néel domain walls and the stability of spin vortices. This work provides new insights into the complex magnetic dynamics of 2D vdW materials at the atomistic scale.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113497"},"PeriodicalIF":3.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657118","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":"Machine learning and density functional theory-based analysis of the surface reactivity of high entropy alloys: The case of H atom adsorption on CoCuFeMnNi","authors":"Allan Abraham B. Padama ,&nbsp;Marianne A. Palmero ,&nbsp;Koji Shimizu ,&nbsp;Tongjai Chookajorn ,&nbsp;Satoshi Watanabe","doi":"10.1016/j.commatsci.2024.113480","DOIUrl":"10.1016/j.commatsci.2024.113480","url":null,"abstract":"<div><div>This study examines the adsorption of H atom on CoCuFeMnNi(111) high entropy alloy (HEA) surface using a combination of density functional theory (DFT) and machine learning (ML) techniques. Hume-Rothery rule, thermodynamic parameters, and electronic structure analysis were utilized to elucidate the stability and reactivity of the CoCuFeMnNi surface. We found that CoCuFeMnNi is a stable solid solution with a fcc structure. By integrating surface microstructure-based input features into our ML model, we accurately predicted H adsorption energies on the hollow sites of CoCuFeMnNi surfaces. Our electronic properties analysis of CoCuFeMnNi revealed that there is an evident interaction among the elements, contributing to a broad range of adsorption energies. During adsorption, the nearest neighbor surface atoms to H directly engage with the adsorbate by transferring charge significantly. The atoms in other regions of the surface contribute through charge redistribution among the surface atoms, influencing overall charge transfer process during H adsorption. We also observed that the average of the <em>d</em>-band centers of the nearest neighbor surface atoms to H influence the adsorption energy, supporting the direct participation of these surface atoms toward adsorption. Our study contributes to a deeper understanding of the influence of surface microstructures on H adsorption on HEAs.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113480"},"PeriodicalIF":3.1,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657115","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":"A study of the role of data and model uncertainty in active learning","authors":"Yahao Li ,&nbsp;Errui Jiang ,&nbsp;Ziqi Ni ,&nbsp;Wudi Li ,&nbsp;Ming Huang ,&nbsp;Fengyuan Zhao ,&nbsp;Fengqi Liu ,&nbsp;Yicong Ye ,&nbsp;Shuxin Bai","doi":"10.1016/j.commatsci.2024.113512","DOIUrl":"10.1016/j.commatsci.2024.113512","url":null,"abstract":"<div><div>Uncertainty-based active learning strategies have demonstrated significant superiority in small data research of materials domain. This study explores the effects of model uncertainty and data uncertainty separately on the performance of active learning strategies, specifically focusing on the number of iterations required to identify the optimal samples. For model uncertainty, three kinds of acquisition functions are compared, including predicted value strategy (PV), ranking of predicted value strategy (PR) and expected improvement strategy (EI). Among these, the active learning model utilizing PR requires the fewest average iterations (1.75). For data uncertainty, we evaluate the iterations of active learning by Gaussian process models that incorporate the uncertainty of the observations and noise samples that takes account into the uncertainty of the input features respectively. The results indicate that the active learning iterations of the three strategies converge to similar at the optimal weighting when the uncertainty of the observations is considered in the model (EI for 1.75, PV for 1.21 and PR for 1.18). In contrast, incorporating noise samples into the augmented dataset after the original samples would severely deteriorate the efficiency of active learning recommendations. Our findings aim to offer guidance for exploring more favorable acquisition functions and methods for active learning strategies.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113512"},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657114","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":"Mo2B2O2-supported Cu and Ni heterogeneous dual atom catalysts for oxygen reduction reactions and oxygen evolution reactions","authors":"Erpeng Wang ,&nbsp;Jian Zhou ,&nbsp;Zhimei Sun","doi":"10.1016/j.commatsci.2024.113505","DOIUrl":"10.1016/j.commatsci.2024.113505","url":null,"abstract":"<div><div>Dual-atom catalysts with heteronuclear active sites (h-DACs) have better potential in oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) than the recognized single atom catalysts (SACs), but the basic understanding of the mechanism is still lacking. Herein, by constructing h-DACs with different distances of Cu and Ni atoms loaded onto MBenes, the metal atoms loading capacity is significantly increased while maintaining the excellent catalytic activity of SACs. The h-DACs possess more flexible active sites compared to SACs. Controlling the distance between the dual atoms is crucial for the catalytic mechanism and activity. The results indicate that the synergistic effect between the dual metal atoms in h-DACs breaks the scaling relationships between the binding energies of the oxygen intermediates, thereby enhancing the ORR and OER catalytic activity of the corresponding SACs. It is worth noting that when the interatomic distance between Cu and Ni atoms is 3.21 Å, ORR is executed through 4e^- *OOH dissociation mechanism and the system exhibits ultra-low ORR and OER overpotentials of 0.16 V and 0.18 V, respectively, far lower than advanced Pt and IrO₂/RuO₂ of 0.45 V and 0.56/0.42 V, making h-DACs a promising ORR/OER bifunctional electrocatalyst. This work not only provides deep insights into the fundamental understanding of reaction mechanism and catalytic activity for h-DACs but also provides guidance for designing efficient catalysts.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113505"},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657143","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":"Enlightenment the dynamic behavior of norbornene–modified ’click’ 4–arm polyethylene glycol hydrogel: Delving into framework properties and transport properties through molecular dynamics simulations","authors":"Rontu Das,&nbsp;Debashis Kundu","doi":"10.1016/j.commatsci.2024.113516","DOIUrl":"10.1016/j.commatsci.2024.113516","url":null,"abstract":"<div><div>The thiol-norbornene cross-linked 4-arm Polyethylene Glycol (PEG) ’click’ hydrogel is a synthetic, sustainable, and bio-inspired polymer extensively used in biomedical applications. Experimental techniques are widely used to study this system, whereas all-atom molecular dynamics simulations, which offer insight into the dynamic behaviours of the system, are never used to study this system. Three models of thiol-norbornene cross-linked PEG ’click’ hydrogel and a base PEG hydrogel are crafted. It is immersed in water to study its structural and transport properties. Structural properties are analysed through root-mean-square deviation (RMSD), radial distribution function (RDF), hydrogen bonds (H-bond), and stress–strain behavior. The RMSD reveals that the norbornene component enhances hydrogel stability compared to the base model. The RDF illustrates interactions between oxygen in PEG chains and water. H-bond results underscore PEG’s strong H-bond acceptance. The norbornene-functionalized crosslinked PEG hydrogel displays maximum H-bonding. It demonstrates a superior swelling ratio attributed to freezing and non-freezing water effects, indicating high stability and potential suitability for applications. Mean square displacements (MSD) unveil the diffusion coefficients of the hydrogel. The base hydrogel model shows the diffusive behavior, and the diffusion coefficient is 1.97 × 10^-10 m²/s. The base model has the highest MSD value compared to other systems. The thiol-norbornene crosslinked click hydrogel has the highest Young’s modulus, which signifies the stiffness of the material. Findings illuminate thiol-norbornene ’click’ PEG hydrogel behaviour, emphasising chemical cross-linking’s crucial role in advanced biomedical applications.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113516"},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657116","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":"Mechanical property prediction of random copolymers using uncertainty-based active learning","authors":"Wei-Che Chang ,&nbsp;Zong-Yun Tsai ,&nbsp;Chin-Wen Chen ,&nbsp;Chi-Hua Yu ,&nbsp;Chuin-Shan Chen","doi":"10.1016/j.commatsci.2024.113489","DOIUrl":"10.1016/j.commatsci.2024.113489","url":null,"abstract":"<div><div>The copolymer, a widely used material in our daily lives, presents a significant challenge in targeted sequence design. While recent advancements in computational simulation and data science offer a promising avenue for addressing this complex issue, challenges persist in labeled data scarcity. In this study, we introduce an uncertainty-based active learning framework for predicting the properties of random copolymers. We found that the active learning strategy allowed for labeling only 40 data points within the design space of 1550 data points, drastically reducing the labeling efforts by 97%. Most data selected by active learning were positioned on the design space’s periphery, transforming the learning task into an interpolation problem. Through integrating active learning and molecular dynamics, we successfully overcame the combinatorial explosion problem in copolymer sequence design, streamlining the data labeling process and culminating in a highly accurate model. This research demonstrates data science’s potential in polymer design, especially when facing data scarcity.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113489"},"PeriodicalIF":3.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657113","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}