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.
本研究基于铝铸造厂希望了解在规格限制范围内合金化学性质微小变化的影响的愿望。为了确保在铸造标准的框架内铸造 A357 铝合金，并最大限度地减少铸造过程中可能出现的质量问题；本研究采用机器学习方法（ML），对合金元素之间虽然在标准范围内但由于极小变化而导致的极限拉伸强度（UTS）、屈服强度（YS）和伸长率（ε）进行了估算。实验建立了低压压铸 (LPDC) A357 Al 合金的化学成分和拉伸性能数据集。选定了 A357 合金中五个输入变量（即主要合金元素 Si 和 Mg 以及最常见的杂质含量 Fe、Ti 和 Cu）之间的关系，并通过自适应神经模糊推理系统（ANFIS）将三个输出变量（即 UTS、YS 和 ε）联系起来。ANFIS 模型预测，影响拉伸性能的最不利因素是铁含量。根据该模型，合金中铁含量之后对 UTS、YS 和 ε 的相对重要性顺序分别为 Si、Mg 和 Ti 含量。
{"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}
Pub Date : 2024-08-06DOI: 10.1016/j.commatsci.2024.113274
Johannes Gerritzen, Andreas Hornig, Peter Winkler, Maik Gude
In this work, we demonstrate how Machine learning (ML) techniques can be employed to externalize the knowledge and time intensive process of material parameter identification. This is done on the example of a recent data driven material model for the non-linear shear behavior of glass fiber reinforced polypropylene (GF/PP) (Gerritzen, 2022). A convolutional neural network (CNN) based model architecture is trained to predict material modeling parameters based on the input of stress–strain-curves. The optimal model architecture and training setup are determined by hyperparameter optimization (HPO). Solely virtual data, generated using the target material model, is used throughout the training and HPO. The final CNN is capable of calculating model parameter combinations from experimental stress–strain-curves which lead to excellent agreement between experimental and associated model curve.
{"title":"A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP","authors":"Johannes Gerritzen, Andreas Hornig, Peter Winkler, Maik Gude","doi":"10.1016/j.commatsci.2024.113274","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113274","url":null,"abstract":"In this work, we demonstrate how Machine learning (ML) techniques can be employed to externalize the knowledge and time intensive process of material parameter identification. This is done on the example of a recent data driven material model for the non-linear shear behavior of glass fiber reinforced polypropylene (GF/PP) (Gerritzen, 2022). A convolutional neural network (CNN) based model architecture is trained to predict material modeling parameters based on the input of stress–strain-curves. The optimal model architecture and training setup are determined by hyperparameter optimization (HPO). Solely virtual data, generated using the target material model, is used throughout the training and HPO. The final CNN is capable of calculating model parameter combinations from experimental stress–strain-curves which lead to excellent agreement between experimental and associated model curve.","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":"141947513","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}
Pub Date : 2024-08-03DOI: 10.1016/j.commatsci.2024.113264
Ying Dou, Koji Shimizu, Hiroshi Fujioka, Satoshi Watanabe
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.
了解缺陷对 III 族氮化物半导体声子相关特性的影响对于器件应用非常重要。然而，电荷态差异对缺陷声子相关特性的影响尚未得到研究。本研究计算了原始晶体和具有 +1 或 +3 氮空位（或）晶体的 AlN 和 GaN 声子带。我们的结果表明，原始晶体和有缺陷晶体的声子带、状态密度（DOS）和红外光谱（IR）存在明显差异，尤其是在和之间。(TO)的红外光谱强度大于(TO)，这与原始晶体和有缺陷晶体的情况不同。在氮化镓的红外光谱中，出现了一个代表缺陷的小峰。这些结果表明，空位对声子相关特性的影响不仅取决于浓度，还取决于电荷状态。这项研究可为今后深入研究缺陷对热性能的影响提供指导。
{"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}
Pub Date : 2024-08-02DOI: 10.1016/j.commatsci.2024.113262
Obioma U. Uche
In the current work, we investigate the transformation mechanics of gold-silver nanoparticles with cuboctahedral and icosahedral geometries by varying relevant attributes including size, composition, morphology, and chemical order. Our findings reveal that the transformation occurs via a martensitic, symmetric mechanism, irrespective of the specific attributes for all nanoparticles under consideration. The associated transformation barriers are observed to be strongly dependent on both size and composition as the activation energies increase with higher silver content. The chemical order is also a significant factor for determining how readily the transformation occurs since core–shell nanoparticles with gold exteriors display higher barriers in comparison to their silver counterparts. Likewise, for a given composition, core–shell morphologies indicate reduced ease of transformation relative to alloy nanoparticles.
{"title":"A DFT analysis of the cuboctahedral to icosahedral transformation of gold-silver nanoparticles","authors":"Obioma U. Uche","doi":"10.1016/j.commatsci.2024.113262","DOIUrl":"https://doi.org/10.1016/j.commatsci.2024.113262","url":null,"abstract":"In the current work, we investigate the transformation mechanics of gold-silver nanoparticles with cuboctahedral and icosahedral geometries by varying relevant attributes including size, composition, morphology, and chemical order. Our findings reveal that the transformation occurs via a martensitic, symmetric mechanism, irrespective of the specific attributes for all nanoparticles under consideration. The associated transformation barriers are observed to be strongly dependent on both size and composition as the activation energies increase with higher silver content. The chemical order is also a significant factor for determining how readily the transformation occurs since core–shell nanoparticles with gold exteriors display higher barriers in comparison to their silver counterparts. Likewise, for a given composition, core–shell morphologies indicate reduced ease of transformation relative to alloy nanoparticles.","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969695","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}
Pub Date : 2024-08-01DOI: 10.1016/j.commatsci.2024.113271
Rongzheng Cai, Ying Xu, Wei Sheng
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.
堆叠不同的二维（2D）材料来构建异质结构被认为是设计高效光催化剂的一种有前途的方法。在本研究中，我们提出了 g-CN/PtSSe 异质结构，并通过第一原理计算研究了其几何、电子和光学性质，从而考察了其作为光催化剂的潜力。结果表明，g-CN/PtSSe 呈 II 型带状排列，并在 g-CN 与 PtSSe 之间建立了内电场，这有利于光生载流子通过 Z 型路径运动。这种相互作用有效地抑制了电荷载流子的重组。氢进化反应（HER）和氧进化反应（OER）中吉布斯自由能的变化表明，g-CN/PtSSe 异质结构能促进光催化水分离的自发反应。值得注意的是，与相应的单层结构相比，g-CN/PtSSe 异质结构具有更高的光吸收率。这些研究结果表明，在可预见的未来，g-CN/PtSSe 异质结构作为一种可行的光催化剂在水分离方面具有巨大的潜力。
{"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}
Pub Date : 2024-07-31DOI: 10.1016/j.commatsci.2024.113256
Akshat Chaudhari, Chakradhar Guntuboina, Hongshuo Huang, Amir Barati Farimani
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.
{"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}
Pub Date : 2024-07-31DOI: 10.1016/j.commatsci.2024.113263
Weiguang Feng, Qingxiao Zhou, Li Wang, Weiwei Ju, Youjing Yang
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.
{"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}
Pub Date : 2024-07-31DOI: 10.1016/j.commatsci.2024.113266
J.R. Eone II, M.T. Ottou Abe, J.M.B. Ndjaka
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.
{"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}
Pub Date : 2024-07-30DOI: 10.1016/j.commatsci.2024.113258
Francisco Dos Reis, Nikolaos Karathanasopoulos
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.
{"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}
Pub Date : 2024-07-29DOI: 10.1016/j.commatsci.2024.113241
Understanding and identifying the defect structure of irradiated materials is of utmost importance to understand the properties of the material. Many experimental techniques exist to detect defects, one of them is Rutherford Backscattering Spectroscopy in channeling mode. This method can reveal the disorder created by defects as a function of depth. However, in order to understand the underlying defect structure resulting in the measured disorder, we need to understand how different defect morphologies affect the experimental signal. In this article we computationally investigate how all commonly found irradiation-induced defect structures in tungsten affect the signal. We found that open volume defects, vacancies and voids, show practically no yield, whereas the interstitials and dislocation loops show significant yields. We was also found that dislocation loop orientation with respect to the RBS/c channeling direction affected the results significantly, where some loops became almost invisible.
{"title":"Understanding the RBS/c spectra of irradiated tungsten: A computational study","authors":"","doi":"10.1016/j.commatsci.2024.113241","DOIUrl":"10.1016/j.commatsci.2024.113241","url":null,"abstract":"<div><p>Understanding and identifying the defect structure of irradiated materials is of utmost importance to understand the properties of the material. Many experimental techniques exist to detect defects, one of them is Rutherford Backscattering Spectroscopy in channeling mode. This method can reveal the disorder created by defects as a function of depth. However, in order to understand the underlying defect structure resulting in the measured disorder, we need to understand how different defect morphologies affect the experimental signal. In this article we computationally investigate how all commonly found irradiation-induced defect structures in tungsten affect the signal. We found that open volume defects, vacancies and voids, show practically no yield, whereas the interstitials and dislocation loops show significant yields. We was also found that dislocation loop orientation with respect to the RBS/c channeling direction affected the results significantly, where some loops became almost invisible.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927025624004622/pdfft?md5=44e8212e953c809979d5537cac0f38d6&pid=1-s2.0-S0927025624004622-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947387","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}
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