Computational Materials Science最新文献_第9页

Adaptive neuro-fuzzy inference system approach for tensile properties prediction of LPDC A357 aluminum alloy 用于 LPDC A357 铝合金拉伸性能预测的自适应神经模糊推理系统方法

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-08-06 DOI: 10.1016/j.commatsci.2024.113275

Onur Al, Fethi Candan, Sennur Candan, Ayse Merve Acilar, Ercan Candan

{"title":"Adaptive neuro-fuzzy inference system approach for tensile properties prediction of LPDC A357 aluminum alloy","authors":"Onur Al, Fethi Candan, Sennur Candan, Ayse Merve Acilar, Ercan Candan","doi":"10.1016/j.commatsci.2024.113275","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113275","url":null,"abstract":"This study is based on the desire of aluminum casting foundries to understand the influence of minor changes, within the specification limits, in the alloy chemistry. In order to ensure the casting of A357 Al alloys within the framework of the casting standards and to minimize the quality problems that may arise during casting; the estimation of ultimate tensile strength (UTS), yield strength (YS) and elongation (ε) due to very small changes among the alloying elements, although they are in the standard range, by using machine learning method (ML), were studied. The dataset of chemical composition and tensile properties of Low-Pressure Die Cast (LPDC) A357 Al alloy were experimentally established. The relationship between five input variables in the A357 alloy, namely the main alloying elements Si and Mg together with the most common impurity contents Fe, Ti and Cu were selected and three outputs (i.e UTS, YS and ε) were linked by Adaptive Neuro Fuzzy Inference System (ANFIS). The ANFIS model predicted that the most detrimental element affecting tensile properties was Fe content. According to this model, the order of the relative importance on UTS, YS and ε revealed as Si, Mg and Ti content respectively after the Fe content of the alloy.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947514","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}

引用次数: 0

A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP 利用机器学习直接识别实验结果参数的方法 - FRP 高度非线性变形行为的实际应用

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-08-06 DOI: 10.1016/j.commatsci.2024.113274

Johannes Gerritzen, Andreas Hornig, Peter Winkler, Maik Gude

引用次数: 0

First-principles study of charge states effects of nitrogen vacancies on phonon properties in III-nitride semiconductors 氮空位对 III 氮化物半导体声子特性的电荷状态影响的第一性原理研究

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-08-03 DOI: 10.1016/j.commatsci.2024.113264

Ying Dou, Koji Shimizu, Hiroshi Fujioka, Satoshi Watanabe

{"title":"First-principles study of charge states effects of nitrogen vacancies on phonon properties in III-nitride semiconductors","authors":"Ying Dou, Koji Shimizu, Hiroshi Fujioka, Satoshi Watanabe","doi":"10.1016/j.commatsci.2024.113264","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113264","url":null,"abstract":"Understanding the effects of defects on the phonon-related properties of III-nitride semiconductors is important for device applications. However, the effect of the charge-state difference on the phonon-related properties of defects has not been studied. This study calculated the phonon bands of AlN and GaN for pristine crystals and crystals with +1 or +3 nitrogen vacancies ( or ). Our results revealed distinct differences in the phonon bands, density of states (DOS), and infrared (IR) spectra between pristine and defective crystals, particularly between and . The exhibited a larger disturbance in the phonon bands than . The exhibited more peaks and larger peak intensities in the DOS than . The IR spectrum intensity of (TO) was larger than that of (TO) in the , which was different from the pristine and cases. In the IR spectrum of in GaN, a small peak appeared to represent a defect. These results imply that the effects of vacancies on the phonon-related properties depend not only on the concentration but also on the charge state. This study can serve as a guide for future in-depth research on the effect of defects on thermal properties.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947375","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}

引用次数: 0

A DFT analysis of the cuboctahedral to icosahedral transformation of gold-silver nanoparticles 金银纳米粒子从立方八面体到二十面体转变的 DFT 分析

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-08-02 DOI: 10.1016/j.commatsci.2024.113262

Obioma U. Uche

引用次数: 0

Janus PtSSe: A promising cocatalyst of g-C3N4 for solar water splitting with improved light absorption and efficient carrier separation Janus PtSSe：一种用于太阳能水分离的前景看好的 g-C3N4 催化剂，具有更好的光吸收能力和更高效的载流子分离能力

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-08-01 DOI: 10.1016/j.commatsci.2024.113271

Rongzheng Cai, Ying Xu, Wei Sheng

{"title":"Janus PtSSe: A promising cocatalyst of g-C3N4 for solar water splitting with improved light absorption and efficient carrier separation","authors":"Rongzheng Cai, Ying Xu, Wei Sheng","doi":"10.1016/j.commatsci.2024.113271","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113271","url":null,"abstract":"Stacking diverse two-dimensional (2D) materials to construct heterostructures is considered to be a promising way for designing efficient photocatalyst. In this study, we proposed g-CN/PtSSe heterostructure and examined its potential as photocatalysts by investigating its geometric, electronic, and optical properties through first-principles calculation. The results show that the g-CN/PtSSe presents type-II band arrangement and establishes an internal electric field from g-CN to PtSSe, which facilitates the movement of photogenerated carriers via the Z-scheme path. This interaction effectively suppresses the recombination of charge carriers. The changes of Gibbs free energy in hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) indicate that the g-CN/PtSSe heterostructure can promote spontaneous reactions of photocatalytic water splitting. Notably, the g-CN/PtSSe heterostructures demonstrate a higher light absorption efficiency to their corresponding monolayer structures. These findings demonstrate that g-CN/PtSSe heterostructure has significant potential as a viable photocatalyst for water splitting in the foreseeable future.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947376","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}

引用次数: 0

AlloyBERT: Alloy property prediction with large language models AlloyBERT：利用大型语言模型进行合金属性预测

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-07-31 DOI: 10.1016/j.commatsci.2024.113256

Akshat Chaudhari, Chakradhar Guntuboina, Hongshuo Huang, Amir Barati Farimani

{"title":"AlloyBERT: Alloy property prediction with large language models","authors":"Akshat Chaudhari, Chakradhar Guntuboina, Hongshuo Huang, Amir Barati Farimani","doi":"10.1016/j.commatsci.2024.113256","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113256","url":null,"abstract":"The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their chemical composition and processing parameters. This study introduces AlloyBERT, a transformer encoder-based model designed to predict properties such as elastic modulus and yield strength of alloys using textual inputs. Leveraging the pre-trained RoBERTa and BERT encoder model as its foundation, AlloyBERT employs self-attention mechanisms to establish meaningful relationships between words, enabling it to interpret human-readable input and predict target alloy properties. By combining a tokenizer trained on our textual data and a RoBERTa encoder pre-trained and fine-tuned for this specific task, we achieved a mean squared error (MSE) of 0.00015 on the Multi Principal Elemental Alloys (MPEA) data set and 0.00527 on the Refractory Alloy Yield Strength (RAYS) dataset using BERT encoder. This surpasses the performance of shallow models, which achieved a best-case MSE of 0.02376 and 0.01459 on the MPEA and RAYS datasets respectively. Our results highlight the potential of language models in material science and establish a foundational framework for text-based prediction of alloy properties that does not rely on complex underlying representations, calculations, or simulations.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947379","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}

引用次数: 0

Influence of defect and doping on the sensitivity and adsorption capacity of Zr2CO2 toward PH3 gas 缺陷和掺杂对 Zr2CO2 对 PH3 气体的敏感性和吸附能力的影响

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-07-31 DOI: 10.1016/j.commatsci.2024.113263

Weiguang Feng, Qingxiao Zhou, Li Wang, Weiwei Ju, Youjing Yang

{"title":"Influence of defect and doping on the sensitivity and adsorption capacity of Zr2CO2 toward PH3 gas","authors":"Weiguang Feng, Qingxiao Zhou, Li Wang, Weiwei Ju, Youjing Yang","doi":"10.1016/j.commatsci.2024.113263","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113263","url":null,"abstract":"In this study, the potential application of the ZrCO-MXene structures as PH sensors and adsorbents for industrial or living applications was investigated using the first-principles approach of density functional theory (DFT). The adsorption of PH on pristine, O-defected, and transition metal (TM; such as Cr, Mn, Fe, Co, Y, Mo, Ru, Rh)-doped ZrCO structures was explored. The results showed that the introduction of TM dopant improved the ZrCO activity more than the O-vacancy. The large adsorption energy, short interaction distance, and high charge transfer suggested chemisorption of PH molecules on TM-doped ZrCO. After the PH molecule was adsorbed, the band gap of ZrCO with O-vacancies, Co-doped ZrCO, and Ru-doped ZrCO decreased by 0.132 eV, and increased by 0.065 eV, 0.073 eV, respectively. The changes in band gap generated an electrical signal that were used for PH detection; thus, ZrCO with O-vacancies and Co– and Ru-doped ZrCO can be used as effective PH sensors because of their high sensitivity. Fe- and Rh-doped ZrCO also showed promising function as adsorbents for PH gas molecules because of their high adsorption stabilities and long recovery times. After adsorption of six PH molecules, their adsorption energies on Fe- and Rh-doped ZrCO were −1.142 eV and −1.135 eV, with recovery times of 1.49 × 10 s and 1.12 × 10 s, respectively. The findings of this study offer novel insights for the development of MXene-based sensors and adsorbents.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947378","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}

引用次数: 0

First-principles study of stability, order and disorder based on an entropy descriptor in noble and ferromagnetic transition metal alloys 基于熵描述符的惰性和铁磁性过渡金属合金稳定性、有序性和无序性第一原理研究

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-07-31 DOI: 10.1016/j.commatsci.2024.113266

J.R. Eone II, M.T. Ottou Abe, J.M.B. Ndjaka

{"title":"First-principles study of stability, order and disorder based on an entropy descriptor in noble and ferromagnetic transition metal alloys","authors":"J.R. Eone II, M.T. Ottou Abe, J.M.B. Ndjaka","doi":"10.1016/j.commatsci.2024.113266","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113266","url":null,"abstract":"Binary alloys composed of ferromagnetic metals (Fe, Co, Ni) and the late noble metals (Rh, Pd, Ag, Ir, Pt, Au) have been investigated using density functional theory with the generalized gradient approximation to understand the role of magnetism in the stability and the order–disorder transition which has an impact on their physicochemical properties, their applications and their possible implementation as precursors of high-entropy alloys. The enthalpy of formation related to the stability demonstrates that all the alloys are more stable in the ferromagnetic phase than in the nonmagnetic phase. The transition from ordered to disordered phases is quantified using a descriptor which is the standard deviation of the energy spectrum of a set of small nanoalloys with random atomic configurations. The study highlights the fact that the entropy-related descriptor, which is a quantity in determining the formation of a disordered phase as a solid solution or an ordered phase is highly dependent on the atomic environment. Despite the fact that the overall variation of this descriptor is supposed to be unpredictable, there is a noticeable trend showing that the environment-dependent ferromagnetism contributes to a chemical order in alloys and nanoalloys and that this order depends on the atomic radius of the species considered. The results indicate that species with small atomic radii, such as nickel, rhodium or iridium are more likely to form solid solutions than species with larger atomic radii and with more delocalized orbitals.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947377","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}

引用次数: 0

Deep learning, deconvolutional neural network inverse design of strut-based lattice metamaterials 基于支柱的晶格超材料的深度学习、去卷积神经网络反设计

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-07-30 DOI: 10.1016/j.commatsci.2024.113258

Francisco Dos Reis, Nikolaos Karathanasopoulos

{"title":"Deep learning, deconvolutional neural network inverse design of strut-based lattice metamaterials","authors":"Francisco Dos Reis, Nikolaos Karathanasopoulos","doi":"10.1016/j.commatsci.2024.113258","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113258","url":null,"abstract":"Machine learning techniques have furnished a new paradigm in the modeling and design of advanced materials, both in the forward prediction of their effective performance and in the inverse identification of designs that meet specific response targets. While numerous architected media with a diverse range of effective mechanical properties have been investigated thus far, the inverse design of beam-based metamaterials with non-uniform inner architectures that emerge as a consequence of evolutionary optimization processes remains a significant challenge. This contribution elaborates a deep learning, deconvolutional neural network based (DCNN) framework which, when combined with a comprehensive parameterization of discrete lattice spaces, enables the inverse engineering of stochastic lattice metamaterials that cover wide mechanical performance spaces. Auxetic, shear soft and stiff, nearly isotropic and highly anisotropic beam-based metamaterial designs are inversely identified, upon a direct request of their desired mechanical performance, without the need of a latent, condensed space representation. The DCNN model is capable of robustly generating beam-based lattice designs with target mechanical attributes that extend beyond those employed in the initial training domain.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947385","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}

引用次数: 0

Understanding the RBS/c spectra of irradiated tungsten: A computational study 了解辐照钨的 RBS/c 光谱：计算研究

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-07-29 DOI: 10.1016/j.commatsci.2024.113241

引用次数: 0