Computational Materials Science最新文献

{"title":"The magnetism of two-dimensional non-magnetic borides Mo2B2 induced by doping and adsorption","authors":"Honghai Zhang,&nbsp;Dongni Wu,&nbsp;Yangfang Liao,&nbsp;Jing Xie","doi":"10.1016/j.commatsci.2025.113680","DOIUrl":"10.1016/j.commatsci.2025.113680","url":null,"abstract":"<div><div>To explore the magnetic properties of two-dimensional non-magnetic borides Mo₂B₂ and expand their applications in the field of spintronics, we investigate, based on first-principles calculations, the electronic structures and magnetic properties of Mo₂B₂ monolayers doped and adsorbed with transition metal (TM = V, Cr, Mn, Fe, Co, Ni) atoms. The results show that both TM-doped and TM-adsorbed Mo₂B₂ monolayers retain metallic properties. Specifically, the Cr-, Mn-, and Fe-doped Mo₂B₂ monolayers exhibit ferromagnetism, with magnetic moments of 1.35 μB, 1.92 μB, and 0.97 μB, respectively. Similarly, when Cr, Mn, Fe, and Co atoms are adsorbed onto the Mo₂B₂ monolayer, these systems also display spin polarization, with magnetic moments of 2.37 μB, 1.70 μB, 1.81 μB, and 1.37 μB, respectively. The ferromagnetism primarily originates from the exchange splitting of the <em>d</em>-orbit of these transition metal atoms. The adsorption of TM atoms on Mo₂B₂ monolayer can induce stronger magnetism than doping. Finally, the positive magnetic anisotropy energies (MAE) of Cr/Mn/Fe-doped and Cr/Mn-adsorbed Mo₂B₂ monolayers indicate in-plane magnetization direction, while the negative MAE of Fe and Co atoms adsorbed on Mo₂B₂ monolayers demonstrate that the magnetization direction tends to be perpendicular to the plane. The MAE of Mn- and Fe-doped Mo₂B₂ monolayers have relatively high values of 7130 μeV and 7990 μeV. Our study provides valuable guidance for experimental work.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113680"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151710","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":"Mixing the transition metals in transition metal diborides","authors":"Xiaochuan Tang ,&nbsp;Gregory B. Thompson ,&nbsp;Christopher R. Weinberger","doi":"10.1016/j.commatsci.2024.113646","DOIUrl":"10.1016/j.commatsci.2024.113646","url":null,"abstract":"<div><div>In this study, the phase behavior of the mixed transition metal diborides is explored and the thermodynamic stability of the equiatomic mixed diborides, also known as the high entropy diborides (HEBs) is investigated by employing the density functional theory (DFT), the quasi-harmonic approximation (QHA), and the calculation-of-the-phase-diagram (CALPHAD) approach. The findings reveal insights into the roles of mixing enthalpy, vibrational entropy and configurational entropy in determining the phase stability of the mixed diborides, paving the ways for the materials design of the mixed diborides beyond the equiatomic compounds. In addition, the detailed discussion of the limitations in the Debye-Grüneisen model for transition metal diborides provides the understanding of the phonon contribution to the thermodynamic stability.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113646"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151102","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":"On the effect of elastic anisotropy and polarizability on solute segregation at low-angle grain boundaries","authors":"Joé Petrazoller,&nbsp;Julien Guénolé,&nbsp;Stéphane Berbenni,&nbsp;Thiebaud Richeton","doi":"10.1016/j.commatsci.2024.113642","DOIUrl":"10.1016/j.commatsci.2024.113642","url":null,"abstract":"<div><div>Solute segregation towards grain boundaries is investigated by modeling solute atoms as elastic dipoles interacting with the strain fields of symmetric tilt low-angle grain boundaries (LAGBs). Elastic dipoles are determined using molecular statics (MS) considering both the permanent second-rank tensor and the fourth-rank polarizability tensor, which is needed to capture the elastic dipole dependence on external strain. For cubic lattices, the latter tensors are related to size and modulus effects, respectively. The strain fields of LAGBs are evaluated either through MS or by considering arrays of edge dislocations within the framework of linear isotropic elasticity or heterogeneous anisotropic elasticity using the Stroh formalism. The interaction energies arising from the coupling between elastic dipoles and LAGB strain fields are compared to segregation energies computed on a site-by-site basis using MS. These comparisons are made for three LAGBs and two cubic systems (Cu and Ag) with solute atoms in substitution (Ag and Ni, respectively). The results underscore the critical role of anisotropic elasticity in accurately modeling solute segregation. Notably, variations in behavior between grain boundaries having a same tilt angle are only captured when anisotropic elasticity is considered. Furthermore, despite the inherent limitations in addressing non-linear effects at defect cores, the elastic dipole approximation proves to be an effective method for approximating segregation energy spectra in LAGBs obtained through atomistic simulations. Lastly, the estimation of overall solute concentration at grain boundaries highlights the prominent influence of the modulus effect.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113642"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151129","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":"Deep-neural-network molecular dynamics investigation of phonon thermal transport in polyether ether ketone","authors":"Haoran Cui,&nbsp;Weijian Hua,&nbsp;Lei Cao,&nbsp;Yifei Jin,&nbsp;Yan Wang","doi":"10.1016/j.commatsci.2024.113641","DOIUrl":"10.1016/j.commatsci.2024.113641","url":null,"abstract":"<div><div>Polyether ether ketone (PEEK) is an important high-performance engineering thermoplastic, yet the thermal transport properties of its crystalline and single-chain forms remain elusive. In this work, a deep neural network interatomic potential is trained using ab initio molecular dynamics to accurately model thermal transport in bulk crystalline, bulk amorphous, and single-chain PEEK. Additionally, phonon thermal transport across chains, which are grouped together through van der Waals (vdW) interactions, exhibits a weak dependence of thermal conductivity (<span><math><mi>κ</mi></math></span>) on the number of chains, i.e., weakly ballistic transport in cross-chain directions. This behavior contrasts with many layered materials bonded by vdW interactions, which often show a strong dependence of cross-plane <span><math><mi>κ</mi></math></span> on the number of layers. This work facilitates the understanding of thermal transport properties of PEEK and phonon transport in vdW-bonded materials in general, offering a theoretical guideline for predicting optimal conditions for PEEK processing and beyond.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113641"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151701","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":"Improving microstructures segmentation via pretraining with synthetic data","authors":"Cuong Ly ,&nbsp;William Frazier ,&nbsp;Adam Olsen ,&nbsp;Ian Schwerdt ,&nbsp;Luther W. McDonald IV ,&nbsp;Alex Hagen","doi":"10.1016/j.commatsci.2024.113639","DOIUrl":"10.1016/j.commatsci.2024.113639","url":null,"abstract":"<div><div>Image analysis of material microstructures through microscopy is an integral capability in the field of materials science. The topological and chemical information obtained through microscopy allow us to draw vital connections between material microstructures, properties, and processing. While scanning electron microscopy (SEM) image is able to yield a considerable wealth of information interpretable by the intuition of experts, there has been significant amount of interests in using machine learning, convolutional neural networks (CNNs) in particular, for such image analysis task. Training CNNs for an image analysis task requires a large annotated dataset. However, in many materials science applications, obtaining a large annotated dataset is cost and labor intensive. In this work, we study the use of synthetic data to enlarge the available annotated experimental data of uranium oxide. We utilize a modified Potts model to simulate uranium oxide particles with morphologies similar to those observed experimentally. We then leverage an image-to-image translation model to synthesize the simulated particles as if they are acquired with SEM. Through this process, we obtain pairs of particle images and their corresponding SEM representations, which corresponds to pairs of annotations and images. Unlike previous works, we leverage synthetic data for pretraining a CNN model prior, and finetune that model further with experimental data. We experimentally demonstrate that using synthetic data as an incremental learning process benefits the overall performance compared to training a model on combined synthetic and experimental data.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113639"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151711","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":"Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film","authors":"Luis Martín-Encinar ,&nbsp;Daniele Lanzoni ,&nbsp;Andrea Fantasia ,&nbsp;Fabrizio Rovaris ,&nbsp;Roberto Bergamaschini ,&nbsp;Francesco Montalenti","doi":"10.1016/j.commatsci.2024.113657","DOIUrl":"10.1016/j.commatsci.2024.113657","url":null,"abstract":"<div><div>A Deep Learning approach is devised to estimate the elastic energy density <span><math><mi>ρ</mi></math></span> at the free surface of an undulated stressed film. About 190000 arbitrary surface profiles <span><math><mrow><mi>h</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> are randomly generated by Perlin noise and paired with the corresponding elastic energy density profiles <span><math><mrow><mi>ρ</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span>, computed by a semi-analytical Green’s function approximation, suitable for small-slope morphologies. The resulting dataset and smaller subsets of it are used for the training of a Fully Convolutional Neural Network. The trained models are shown to return quantitative predictions of <span><math><mi>ρ</mi></math></span>, not only in terms of convergence of the loss function during training, but also in validation and testing, with better results in the case of the larger dataset. Extensive tests are performed to assess the generalization capability of the Neural Network model when applied to profiles with localized features or assigned geometries not included in the original dataset. Moreover, its possible exploitation on domain sizes beyond the one used in the training is also analyzed in-depth. The conditions providing a one-to-one reproduction of the “ground-truth” <span><math><mrow><mi>ρ</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> profiles computed by the Green’s approximation are highlighted along with critical cases. The accuracy and robustness of the deep-learned <span><math><mrow><mi>ρ</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> are further demonstrated in the time-integration of surface evolution problems described by simple partial differential equations of evaporation/condensation and surface diffusion.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113657"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151783","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":"Unveiling the Superiority of naphthylene over biphenylene in silicon carbide 2D Architectures","authors":"Jose A.S. Laranjeira ,&nbsp;Nicolas F. Martins ,&nbsp;Sérgio A. Azevedo ,&nbsp;Pablo A. Denis ,&nbsp;Julio R. Sambrano","doi":"10.1016/j.commatsci.2025.113743","DOIUrl":"10.1016/j.commatsci.2025.113743","url":null,"abstract":"<div><div>In recent years, silicon carbide (SiC) has once again become a target of interest in the materials science community, this time with particular interest in two-dimensional materials, which have attracted attention due to their large surface area and infinitesimal volume. In this sense, this study introduces a novel SiC structure based on the recently reported naphthylene lattice, termed INP-SiC. It compares its electronic, mechanical and vibrational properties with the well-reported biphenylene-like SiC (BPN-SiC) via density functional theory (DFT) simulations. Both monolayers are stable at 300 K and exhibit high electron mobility, with INP-SiC reaching 94.890 10² cm²/V.s. INP-SiC also shows superior mechanical robustness, with Young’s modulus (171.65 N/m) comparable to g-SiC (178.02 N/m) and T-SiC (182.22 N/m). Overall, this work is dedicated to showing the INP-SiC potential as a multifunctional 2D platform.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"251 ","pages":"Article 113743"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143290241","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":"Manipulating electronic, magnetic and optical properties of C3N monolayer through doping a 4d series transition metal atom","authors":"Su-Fang Wang ,&nbsp;Dan Xue ,&nbsp;Li-Yong Chen ,&nbsp;You Xie ,&nbsp;Jian-Min Zhang ,&nbsp;Jing Liang","doi":"10.1016/j.commatsci.2024.113652","DOIUrl":"10.1016/j.commatsci.2024.113652","url":null,"abstract":"<div><div>The influence of introducing a 4<em>d</em> series transition metal atom into the C₃N monolayer on its electronic structure, magnetic and optical characteristics is studied using density functional theory. With an indirect band gap of 1.128 eV, the C₃N monolayer has semiconductor properties, and the introduction of defect significantly enhances its conductivity. By substituting C or N atom with transition metals, the electronic structure was diversified, with doping by Y, Ag, and Cd resulting in a transformation to a metallic state, while Tc doping exhibited semi-metallic characteristics. When transition metal modifications are applied to the surface of C₃N, the majority of the systems exhibit spin-polarization phenomena, displaying characteristics of dilute magnetic semiconductors. Furthermore, substitutional doping was found to open new opportunities for the application of C₃N materials in the infrared region. Therefore, various 4<em>d</em> transition metal atoms can be employed for the modification of monolayer C₃N through different methods, providing strong support for the development of magnetic nanoscale and spin-based electronic devices.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113652"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151104","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":"Lorenz number and electronic thermoelectric figure of Merit: Thermodynamics and direct DFT calculations","authors":"Yi Wang,&nbsp;Jorge Paz Soldan Palma,&nbsp;Shun-Li Shang,&nbsp;Long-Qing Chen,&nbsp;Zi-Kui Liu","doi":"10.1016/j.commatsci.2024.113647","DOIUrl":"10.1016/j.commatsci.2024.113647","url":null,"abstract":"<div><div>The Lorenz number (<span><math><mrow><mi>L</mi></mrow></math></span>) contained in the Wiedemann–Franz law represents the ratio of two kinetic parameters of electronic charge carriers: the electronic contribution to the thermal conductivity (<span><math><mrow><msub><mi>K</mi><mrow><mi>el</mi></mrow></msub></mrow></math></span>) and the electrical conductivity (σ), and can be expressed as <span><math><mrow><mi>LT</mi><mo>=</mo><msub><mi>K</mi><mrow><mi>el</mi></mrow></msub><mo>/</mo><mi>σ</mi></mrow></math></span> where <span><math><mrow><mi>T</mi></mrow></math></span> is temperature. We demonstrate that the Lorenz number simply equals to the ratio of two thermodynamic quantities: the electronic heat capacity (<span><math><mrow><msub><mi>c</mi><mrow><mi>el</mi></mrow></msub><mrow><mo>)</mo></mrow></mrow></math></span> and the electrochemical capacitance (<span><math><mrow><msub><mi>c</mi><mi>N</mi></msub><mrow><mo>)</mo></mrow></mrow></math></span> through <span><math><mrow><mi>LT</mi><mo>=</mo><msub><mi>c</mi><mrow><mi>el</mi></mrow></msub><mo>/</mo><msub><mi>c</mi><mi>N</mi></msub></mrow></math></span>, a purely thermodynamic quantity, and thus it can be calculated solely based on the electron density of states of a material. It is shown that our thermodynamic formulation for the Lorenz number leads to: i) the well-known Sommerfeld value <span><math><mrow><mi>L</mi><mo>=</mo><msup><mrow><mi>π</mi></mrow><mn>2</mn></msup><mo>/</mo><mn>3</mn><msup><mrow><mfenced><mrow><msub><mi>k</mi><mi>B</mi></msub><mo>/</mo><mi>e</mi></mrow></mfenced></mrow><mn>2</mn></msup></mrow></math></span> at low temperature limit; ii) the Drude value <span><math><mrow><mi>L</mi><mo>=</mo><mrow><mfenced><mrow><mn>3</mn><mo>/</mo><mn>2</mn></mrow></mfenced></mrow><msup><mrow><mfenced><mrow><msub><mi>k</mi><mi>B</mi></msub><mo>/</mo><mi>e</mi></mrow></mfenced></mrow><mn>2</mn></msup></mrow></math></span> at the high temperature limit with the free electron gas model, and iii) possible higher values than the Sommerfeld limit for certain semiconductors. Importantly, we demonstrate that the purely electronic contribution to the thermoelectric figure-of-merit can be directly and efficiently computed using high-throughput density functional theory (DFT) calculations, eliminating the need for the computationally intensive Boltzmann transport theory for electronic thermal and electrical conductivities. For thermoelectric materials with low or negligible lattice thermal conductivity, this approach provides a rapid and reliable estimation of the thermoelectric figure-of-merit. These findings highlight the utility of the proposed methodology in high-throughput workflows for thermoelectric material discovery and screening.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113647"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151131","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":"First-principles prediction of high-performance thermoelectric nitrides","authors":"Xinxin Yan ,&nbsp;Xuan Zheng ,&nbsp;Wei Cao ,&nbsp;Haohuan Li","doi":"10.1016/j.commatsci.2025.113678","DOIUrl":"10.1016/j.commatsci.2025.113678","url":null,"abstract":"<div><div>Nitride thermoelectric materials have garnered significant research interest due to their abundant availability, low cost, and low toxicity. However, compared to traditional thermoelectric materials, the range of nitride-based options remains limited. In this study, we performed a systematic screening of potential nitride thermoelectric materials using first-principles calculations. The screening identified several promising candidates, including Ca₃BiN, Sr₃BiN, Sr₃SbN, and LiZnN, all of which exhibit low lattice thermal conductivity (below 1 W/mK at 300 K). Subsequent evaluation of their thermoelectric properties revealed that Ca₃BiN, Sr₃BiN, and Sr₃SbN show strong n-type and p-type performance, while LiZnN demonstrates enhanced p-type performance, attributed to its wider band gap, but poor n-type performance. Expanding the scope to A₃BN (A = Mg, Ca, Sr; B = P, As, Sb, Bi) structures, we found that Mg-based compositions exhibited the best thermoelectric properties, following consistent trends across the examined structures. These results provide a theoretical basis for the development of high-performance nitride thermoelectric materials.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113678"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151134","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}