Pub Date : 2024-10-09DOI: 10.1016/j.commatsci.2024.113421
Chunshan He , Weiliang Wang
Based on the density functional theory (DFT) computing method, a kind of bulk carbon allotrope consisting of non-coplanar pentagon carbon atom rings was predicted. The helical polarization Raman spectroscopies are got by numerical calculation. The physical properties, such as band structures, elastic tensors and thermal conductivity tensors, are calculated and compared with the diamond and the tetragonal crystal structure of carbon (T12C).
{"title":"Bulk pentagon carbon allotrope and its properties","authors":"Chunshan He , Weiliang Wang","doi":"10.1016/j.commatsci.2024.113421","DOIUrl":"10.1016/j.commatsci.2024.113421","url":null,"abstract":"<div><div>Based on the density functional theory (DFT) computing method, a kind of bulk carbon allotrope consisting of non-coplanar pentagon carbon atom rings was predicted. The helical polarization Raman spectroscopies are got by numerical calculation. The physical properties, such as band structures, elastic tensors and thermal conductivity tensors, are calculated and compared with the diamond and the tetragonal crystal structure of carbon (T12C).</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113421"},"PeriodicalIF":3.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424767","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 : 2024-10-09DOI: 10.1016/j.commatsci.2024.113435
Apoorva, Pankaj Kandwal
One of the key strategies for development of next-generation high-performance rechargeable batteries involves exploring novel anode materials. In this paper, through meticulous first-principles calculations, the potential of B2C monolayer as an anode material in potassium-ion batteries (KIBs) has been explored. By assessing formation energy, the stability of stand-alone B2C monolayer was evaluated. Our calculations showed metallic properties of the B2C monolayer which makes it particularly advantageous for energy storage, ensuring robust electronic conductivity during charging and discharging process of battery. Molecular dynamics simulations have also been performed to study thermal stability of pristine and potassinated B2C monolayer. Remarkably, the B2C monolayer surpasses conventional two-dimensional (2D) materials in terms of diffusion energy barrier, and storage capacity. With a theoretical specific capacity (TSC) of 796.9 mAhg−1, along with low diffusion barrier of 0.07 eV, B2C monolayer emerges as a promising anode material in KIBs.
{"title":"Harnessing two dimensional B2C monolayer as an anode material in potassium ion batteries: DFT and AIMD study","authors":"Apoorva, Pankaj Kandwal","doi":"10.1016/j.commatsci.2024.113435","DOIUrl":"10.1016/j.commatsci.2024.113435","url":null,"abstract":"<div><div>One of the key strategies for development of next-generation high-performance rechargeable batteries involves exploring novel anode materials. In this paper, through meticulous first-principles calculations, the potential of B<sub>2</sub>C monolayer as an anode material in potassium-ion batteries (KIBs) has been explored. By assessing formation energy, the stability of stand-alone B<sub>2</sub>C monolayer was evaluated. Our calculations showed metallic properties of the B<sub>2</sub>C monolayer which makes it particularly advantageous for energy storage, ensuring robust electronic conductivity during charging and discharging process of battery. Molecular dynamics simulations have also been performed to study thermal stability of pristine and potassinated B<sub>2</sub>C monolayer. Remarkably, the B<sub>2</sub>C monolayer surpasses conventional two-dimensional (2D) materials in terms of diffusion energy barrier, and storage capacity. With a theoretical specific capacity (TSC) of 796.9 mAhg<sup>−1</sup>, along with low diffusion barrier of 0.07 eV, B<sub>2</sub>C monolayer emerges as a promising anode material in KIBs.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113435"},"PeriodicalIF":3.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424768","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 : 2024-10-08DOI: 10.1016/j.commatsci.2024.113426
Martin Eberle , Samuel Pinches , Wesley Kean Wah Tai , Pablo Guzman , Hannah King , Hailing Zhou , Andrew Ang
The desired porosity level of cold-sprayed titanium parts varies depending on the application and therefore requires precise control. To achieve the desired porosity the selection of the correct spray parameters is essential. This study investigates how the cold spraying process affects porosity levels through the application of machine learning techniques. 14 parameters are recorded during the cold spraying process of titanium parts, with the porosity level of each process being manually measured through the analysis of microscope images. Due to the high cost associated with generating data, the dataset size was limited for this study. To alleviate this problem such that machine learning models can be properly trained, this paper carefully enhances a firsthand dataset by using feature engineering, feature selection, and dimension reduction techniques. The study implemented random forest, gradient boosting, and neural network algorithms, with the neural network model demonstrating the best performance. This model achieved an RMSE of 0.7 % on unseen data. For the spray parameter ranges of the available dataset, based on the Shapley value analysis, the spray angle has been identified as the most influential feature of the model for predicting porosity.
{"title":"Porosity prediction of cold sprayed titanium parts using machine learning","authors":"Martin Eberle , Samuel Pinches , Wesley Kean Wah Tai , Pablo Guzman , Hannah King , Hailing Zhou , Andrew Ang","doi":"10.1016/j.commatsci.2024.113426","DOIUrl":"10.1016/j.commatsci.2024.113426","url":null,"abstract":"<div><div>The desired porosity level of cold-sprayed titanium parts varies depending on the application and therefore requires precise control. To achieve the desired porosity the selection of the correct spray parameters is essential. This study investigates how the cold spraying process affects porosity levels through the application of machine learning techniques. 14 parameters are recorded during the cold spraying process of titanium parts, with the porosity level of each process being manually measured through the analysis of microscope images. Due to the high cost associated with generating data, the dataset size was limited for this study. To alleviate this problem such that machine learning models can be properly trained, this paper carefully enhances a firsthand dataset by using feature engineering, feature selection, and dimension reduction techniques. The study implemented random forest, gradient boosting, and neural network algorithms, with the neural network model demonstrating the best performance. This model achieved an RMSE of 0.7 % on unseen data. For the spray parameter ranges of the available dataset, based on the Shapley value analysis, the spray angle has been identified as the most influential feature of the model for predicting porosity.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113426"},"PeriodicalIF":3.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424769","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 : 2024-10-08DOI: 10.1016/j.commatsci.2024.113428
Regina M. Burganova , Zafari Umar , Oleg V. Nedopekin , Ilya V. Chepkasov , Irina I. Piyanzina
Comprehensive systematic density functional theory calculations were performed for five typical rare earth trifluorides, namely GdF3, TbF3, DyF3, HoF3, and ErF3, under pressures up to 30 GPa, demonstrating induced phase transitions in agreement with available experimental observations. For the first time the careful check of simulation routine is performed for the selected set of rare earth trifluorides. An extensive selection of the methodology parameters revealed different behaviors for the systems under study. Based on comparative analysis with available experimental data, suitable computation details were suggested for further calculations. For the selected trifluorides, the evolution of lattice parameters and volume, criteria of stability, and elastic stiffness coefficients were analyzed with pressure, which also were calculated for the first time. Additionally, electronic, magnetic, and optical features were captured within the scope of the work for all five compounds in two phases, along with a comparative analysis with experimental data where available.
{"title":"Complex investigation of XF3(X = Gd, Tb, Dy, Ho and Er) fluorides under pressure: An ab-initio perspective","authors":"Regina M. Burganova , Zafari Umar , Oleg V. Nedopekin , Ilya V. Chepkasov , Irina I. Piyanzina","doi":"10.1016/j.commatsci.2024.113428","DOIUrl":"10.1016/j.commatsci.2024.113428","url":null,"abstract":"<div><div>Comprehensive systematic density functional theory calculations were performed for five typical rare earth trifluorides, namely GdF<sub>3</sub>, TbF<sub>3</sub>, DyF<sub>3</sub>, HoF<sub>3</sub>, and ErF<sub>3</sub>, under pressures up to 30<!--> <!-->GPa, demonstrating induced phase transitions in agreement with available experimental observations. For the first time the careful check of simulation routine is performed for the selected set of rare earth trifluorides. An extensive selection of the methodology parameters revealed different behaviors for the systems under study. Based on comparative analysis with available experimental data, suitable computation details were suggested for further calculations. For the selected trifluorides, the evolution of lattice parameters and volume, criteria of stability, and elastic stiffness coefficients were analyzed with pressure, which also were calculated for the first time. Additionally, electronic, magnetic, and optical features were captured within the scope of the work for all five compounds in two phases, along with a comparative analysis with experimental data where available.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113428"},"PeriodicalIF":3.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424693","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 : 2024-10-08DOI: 10.1016/j.commatsci.2024.113440
Oscar Hurtado-Aular , Ricardo M. Ferullo , Patricia G. Belelli
Density functional calculations with dispersion corrections (DFT-D) have been performed to study the adsorption and dissociation of CO2 on W3Ox/Cu(111) inverse catalyst (x = 9 or 6). The W3O9 aggregate adsorbs in several different geometries through the formation of O-Cu bonds, in all the cases taking electronic charge from the metal surface. The reduced W3O6 particle anchors very strongly to Cu by means of W-Cu bonds; in this case, the charge transfer is opposite than for W3O9/Cu yielding the oxide particle positively charged. CO2 is activated on W3O6/Cu(111) at the oxide/metal interface; its dissociation was found to be exothermic and kinetically more favorable than on the pure counterparts, Cu(111) and WO3(001) surfaces. In contrast, CO2 is activated on W3O9/Cu(111) only in the form that is by far the least stable (the one possessing Cs symmetry). Our results suggest that stoichiometry and symmetry of Cu-supported W3Ox clusters play a crucial role in CO2 activation and dissociation. In particular, the mixed W3O6/Cu(111) system appears as a catalyst of great potential for reactions involving CO2 dissociation.
{"title":"Cu(111)-supported W3Ox clusters: Stoichiometry and symmetry effects on CO2 activation and dissociation","authors":"Oscar Hurtado-Aular , Ricardo M. Ferullo , Patricia G. Belelli","doi":"10.1016/j.commatsci.2024.113440","DOIUrl":"10.1016/j.commatsci.2024.113440","url":null,"abstract":"<div><div>Density functional calculations with dispersion corrections (DFT-D) have been performed to study the adsorption and dissociation of CO<sub>2</sub> on W<sub>3</sub>O<sub>x</sub>/Cu(111) inverse catalyst (x = 9 or 6). The W<sub>3</sub>O<sub>9</sub> aggregate adsorbs in several different geometries through the formation of O-Cu bonds, in all the cases taking electronic charge from the metal surface. The reduced W<sub>3</sub>O<sub>6</sub> particle anchors very strongly to Cu by means of W-Cu bonds; in this case, the charge transfer is opposite than for W<sub>3</sub>O<sub>9</sub>/Cu yielding the oxide particle positively charged. CO<sub>2</sub> is activated on W<sub>3</sub>O<sub>6</sub>/Cu(111) at the oxide/metal interface; its dissociation was found to be exothermic and kinetically more favorable than on the pure counterparts, Cu(111) and WO<sub>3</sub>(001) surfaces. In contrast, CO<sub>2</sub> is activated on W<sub>3</sub>O<sub>9</sub>/Cu(111) only in the form that is by far the least stable (the one possessing C<sub>s</sub> symmetry). Our results suggest that stoichiometry and symmetry of Cu-supported W<sub>3</sub>O<sub>x</sub> clusters play a crucial role in CO<sub>2</sub> activation and dissociation. In particular, the mixed W<sub>3</sub>O<sub>6</sub>/Cu(111) system appears as a catalyst of great potential for reactions involving CO<sub>2</sub> dissociation.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113440"},"PeriodicalIF":3.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424692","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}
We investigated the diffusion barrier, diffusion pathways, and electromotive force when using a magnesium-tin alloy oxide, which does not include rare earth elements, as the cathode material. We employed molecular dynamics to amorphize the crystalline structure, followed by the nudged elastic band method to determine diffusion pathways and diffusion barriers. It became evident that amorphization led magnesium to occupy tetrahedral sites, diffusing between octahedral sites formed by tin and oxygen. The effective diffusion barrier is suggested to be lower than that of lithium in cathodes. Based on the difference in internal energy, electromotive force of around 3 V was estimated.
{"title":"Estimation of magnesium diffusion pathways and diffusion barriers within the cathode of tin-magnesium oxide system","authors":"Shuhei Inoue , Ko Suzuki , Hideaki Kambara , Yukihiko Matsumura","doi":"10.1016/j.commatsci.2024.113437","DOIUrl":"10.1016/j.commatsci.2024.113437","url":null,"abstract":"<div><div>We investigated the diffusion barrier, diffusion pathways, and electromotive force when using a magnesium-tin alloy oxide, which does not include rare earth elements, as the cathode material. We employed molecular dynamics to amorphize the crystalline structure, followed by the nudged elastic band method to determine diffusion pathways and diffusion barriers. It became evident that amorphization led magnesium to occupy tetrahedral sites, diffusing between octahedral sites formed by tin and oxygen. The effective diffusion barrier is suggested to be lower than that of lithium in cathodes. Based on the difference in internal energy, electromotive force of around 3 V was estimated.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113437"},"PeriodicalIF":3.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424765","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}
This study employs diffusion generative models to reconstruct random representative volume elements (RVEs) of unidirectional composites with noncircular fibers. Microscope images of these composites were prepared and trained with denoising diffusion probabilistic model (DDPM), denoising diffusion implicit model (DDIM), progressive distillation diffusion model (PDDM), and deep convolutional generative adversarial network (DCGAN). Hyperparameter tuning was performed for both DDPM and PDDM, and the generated RVE images were evaluated using the two-point correlation function (TPCF), Fréchet Inception Distance (FID), and computational cost. Furthermore, finite element (FE) models were generated using these images, and FE simulations were conducted considering interfacial debonding behavior. The resulting stress and strain curves from these simulations were compared. The results show that DDPM demonstrated the best performance in final image quality, while PDDM maintained stable performance from the early stages of training. Additionally, both models exhibited excellent agreement with the original images, indicating high quality, diversity, and resemblance.
{"title":"A comprehensive investigation on the performance of reconstruction of noncircular fiber-representative volume elements in unidirectional composites using diffusion generative models","authors":"Seong-Won Jin, Hong-Kyun Noh, Myeong-Seok Go, Jae Hyuk Lim","doi":"10.1016/j.commatsci.2024.113441","DOIUrl":"10.1016/j.commatsci.2024.113441","url":null,"abstract":"<div><div>This study employs diffusion generative models to reconstruct random representative volume elements (RVEs) of unidirectional composites with noncircular fibers. Microscope images of these composites were prepared and trained with denoising diffusion probabilistic model (DDPM), denoising diffusion implicit model (DDIM), progressive distillation diffusion model (PDDM), and deep convolutional generative adversarial network (DCGAN). Hyperparameter tuning was performed for both DDPM and PDDM, and the generated RVE images were evaluated using the two-point correlation function (TPCF), Fréchet Inception Distance (FID), and computational cost. Furthermore, finite element (FE) models were generated using these images, and FE simulations were conducted considering interfacial debonding behavior. The resulting stress and strain curves from these simulations were compared. The results show that DDPM demonstrated the best performance in final image quality, while PDDM maintained stable performance from the early stages of training. Additionally, both models exhibited excellent agreement with the original images, indicating high quality, diversity, and resemblance.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113441"},"PeriodicalIF":3.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424770","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 : 2024-10-07DOI: 10.1016/j.commatsci.2024.113425
Konstantina Traka , Estefanía Sepúlveda Hernández , Tuan Nguyen-Minh , Karo Sedighiani , Jilt Sietsma , Leo A.I. Kestens
<div><div>This work investigates the formation of the recrystallisation microstructure and texture of various single-phase ferrite low-carbon steels that were rolled at different temperatures and of which the deformation microstructure was characterized by high resolution electron backscatter diffraction (EBSD). Three cases are considered: (i) cold-rolled interstitial-free (IF) steel, warm-rolled IF steel at 550 <span><math><mtext>°C</mtext></math></span> and warm rolled Fe-Si steel at 900 <span><math><mtext>°C</mtext></math></span> (below the austenitization temperature due to Si). It is well-known that the deformation texture after flat rolling of single-ferrite low carbon steels exhibits the characteristic <span><math><mi>α</mi></math></span>/<span><math><mi>γ</mi></math></span>-fiber texture, i.e. <span><math><mrow><mo><</mo><mn>110</mn><mo>></mo></mrow></math></span>//Rolling Direction (RD) and <span><math><mrow><mo><</mo><mn>111</mn><mo>></mo></mrow></math></span>//Normal Direction (ND), irrespective of the rolling temperature, as long as there is no concurrent phase transformation. However, different recrystallisation textures appear as a function of the rolling temperature. Generally speaking, the <span><math><mi>γ</mi></math></span>-fiber recrystallisation texture is obtained after cold rolling, whereas the <span><math><mi>θ</mi></math></span>-fiber components ( <span><math><mrow><mo><</mo><mn>100</mn><mo>></mo></mrow></math></span>//ND) intensify at the expense of the <span><math><mi>γ</mi></math></span>-fiber orientations with increasing rolling temperature. Although these phenomena are well-known, the reasons for this behavior in terms of preferential orientation selection remain as yet unclear. In the present paper, recrystallisation microstructures and textures are simulated with a full-field cellular-automaton (CA) description, whereby recrystallisation from its incipient stage is considered as a process of sub-grain coarsening controlled by the well-known physical laws of driving force and kinetics. The simulations integrate in one single model the various conditions that give rise to the observed temperature dependence of the evolving static recrystallisation texture and microstructure. The different rolling temperatures will give rise to different initial microstructures at the onset of recrystallisation with noticeable variations in short-range orientation gradients in <span><math><mi>γ</mi></math></span> and <span><math><mi>θ</mi></math></span>-fiber orientations, respectively. The mere application of local grain-boundary migration laws on the topology of the deformation structure, without imposing any specific nucleation selection criterion, will properly balance the dominance of <span><math><mi>γ</mi></math></span>-fiber grains after cold-rolling and <span><math><mi>θ</mi></math></span>-fiber orientations after warm rolling. Finally, the well-known nucleation of Goss orientations (<span><math><mrow><mrow><m
{"title":"Prediction of different recrystallisation textures under a single unified physics-based model description","authors":"Konstantina Traka , Estefanía Sepúlveda Hernández , Tuan Nguyen-Minh , Karo Sedighiani , Jilt Sietsma , Leo A.I. Kestens","doi":"10.1016/j.commatsci.2024.113425","DOIUrl":"10.1016/j.commatsci.2024.113425","url":null,"abstract":"<div><div>This work investigates the formation of the recrystallisation microstructure and texture of various single-phase ferrite low-carbon steels that were rolled at different temperatures and of which the deformation microstructure was characterized by high resolution electron backscatter diffraction (EBSD). Three cases are considered: (i) cold-rolled interstitial-free (IF) steel, warm-rolled IF steel at 550 <span><math><mtext>°C</mtext></math></span> and warm rolled Fe-Si steel at 900 <span><math><mtext>°C</mtext></math></span> (below the austenitization temperature due to Si). It is well-known that the deformation texture after flat rolling of single-ferrite low carbon steels exhibits the characteristic <span><math><mi>α</mi></math></span>/<span><math><mi>γ</mi></math></span>-fiber texture, i.e. <span><math><mrow><mo><</mo><mn>110</mn><mo>></mo></mrow></math></span>//Rolling Direction (RD) and <span><math><mrow><mo><</mo><mn>111</mn><mo>></mo></mrow></math></span>//Normal Direction (ND), irrespective of the rolling temperature, as long as there is no concurrent phase transformation. However, different recrystallisation textures appear as a function of the rolling temperature. Generally speaking, the <span><math><mi>γ</mi></math></span>-fiber recrystallisation texture is obtained after cold rolling, whereas the <span><math><mi>θ</mi></math></span>-fiber components ( <span><math><mrow><mo><</mo><mn>100</mn><mo>></mo></mrow></math></span>//ND) intensify at the expense of the <span><math><mi>γ</mi></math></span>-fiber orientations with increasing rolling temperature. Although these phenomena are well-known, the reasons for this behavior in terms of preferential orientation selection remain as yet unclear. In the present paper, recrystallisation microstructures and textures are simulated with a full-field cellular-automaton (CA) description, whereby recrystallisation from its incipient stage is considered as a process of sub-grain coarsening controlled by the well-known physical laws of driving force and kinetics. The simulations integrate in one single model the various conditions that give rise to the observed temperature dependence of the evolving static recrystallisation texture and microstructure. The different rolling temperatures will give rise to different initial microstructures at the onset of recrystallisation with noticeable variations in short-range orientation gradients in <span><math><mi>γ</mi></math></span> and <span><math><mi>θ</mi></math></span>-fiber orientations, respectively. The mere application of local grain-boundary migration laws on the topology of the deformation structure, without imposing any specific nucleation selection criterion, will properly balance the dominance of <span><math><mi>γ</mi></math></span>-fiber grains after cold-rolling and <span><math><mi>θ</mi></math></span>-fiber orientations after warm rolling. Finally, the well-known nucleation of Goss orientations (<span><math><mrow><mrow><m","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113425"},"PeriodicalIF":3.1,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424694","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}
Pub Date : 2024-10-06DOI: 10.1016/j.commatsci.2024.113420
Mengshuang Fu , Xingfan Zhang , Qingshui Liu , Jian Huang , Zhichao Li , Zhihao Wang , Weikang Wu , Hui Li
The Fe-Ni Invar alloy is often employed under extreme conditions such as high temperatures, high pressures, and corrosive environments, making it susceptible to oxidation and subsequent failure. Hence, a thorough understanding of its oxidation behavior is crucial. We performed ReaxFF-based molecular dynamics (MD) simulations to study the oxidation behaviour of Fe-Ni Invar alloy at the atomic scale under extreme conditions. The initial stage of oxidation involves the preferential adsorption and dissociation of O2, demonstrating its surface-site selectivity. The initial oxidation kinetics follows a logarithmic law, and the whole oxidation process results in dominant low-coordinated clusters configurations in the oxide film. Simultaneously, Fe atoms tend to donate more electrons to O atoms than Ni atoms. Moreover, the O2 consumption rate were found to increase with pressure and amorphous oxides formed more readily under high pressure. Our results indicate that adjusting the pressure may enhance the oxidation resistance of the Fe-Ni alloy, which is significant for the design and application of alloys in extreme conditions.
铁-镍英华尔合金经常在高温、高压和腐蚀性环境等极端条件下使用，因此很容易发生氧化，进而导致失效。因此，彻底了解其氧化行为至关重要。我们进行了基于 ReaxFF 的分子动力学（MD）模拟，以研究铁-镍因瓦合金在极端条件下的原子尺度氧化行为。氧化的初始阶段涉及 O2 的优先吸附和解离，表明了其表面-位点选择性。初始氧化动力学遵循对数规律，整个氧化过程导致氧化膜中的低配位簇构型占主导地位。与此同时，Fe 原子往往比 Ni 原子向 O 原子提供更多的电子。此外，还发现 O2 的消耗率随压力的增加而增加，并且在高压下更容易形成无定形氧化物。我们的研究结果表明，调节压力可增强铁-镍合金的抗氧化性，这对极端条件下合金的设计和应用具有重要意义。
{"title":"Oxidation behavior of Fe-Ni Invar alloy under high pressure: A ReaxFF molecular dynamics study","authors":"Mengshuang Fu , Xingfan Zhang , Qingshui Liu , Jian Huang , Zhichao Li , Zhihao Wang , Weikang Wu , Hui Li","doi":"10.1016/j.commatsci.2024.113420","DOIUrl":"10.1016/j.commatsci.2024.113420","url":null,"abstract":"<div><div>The Fe-Ni Invar alloy is often employed under extreme conditions such as high temperatures, high pressures, and corrosive environments, making it susceptible to oxidation and subsequent failure. Hence, a thorough understanding of its oxidation behavior is crucial. We performed ReaxFF-based molecular dynamics (MD) simulations to study the oxidation behaviour of Fe-Ni Invar alloy at the atomic scale under extreme conditions. The initial stage of oxidation involves the preferential adsorption and dissociation of O<sub>2</sub>, demonstrating its surface-site selectivity. The initial oxidation kinetics follows a logarithmic law, and the whole oxidation process results in dominant low-coordinated clusters configurations in the oxide film. Simultaneously, Fe atoms tend to donate more electrons to O atoms than Ni atoms. Moreover, the O<sub>2</sub> consumption rate were found to increase with pressure and amorphous oxides formed more readily under high pressure. Our results indicate that adjusting the pressure may enhance the oxidation resistance of the Fe-Ni alloy, which is significant for the design and application of alloys in extreme conditions.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113420"},"PeriodicalIF":3.1,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424695","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 : 2024-10-05DOI: 10.1016/j.commatsci.2024.113430
Shuai Wang, Xunwei Zuo, Nailu Chen, Yonghua Rong
The martensite start () temperature, plays a significant role in guiding the alloy design and heat treatment process for steels. However, due to the complexity of martensite transformation, it remains challenging to establish more generalized models. In this study, the database with three scales was first built by multi-scale data mining, without relying on thermodynamic software. Then a convolutional neural network (CNN) model, as well as four traditional machine learning (ML) models, were trained to predict using multi-scale database. The CNN model exhibits the smallest error, and the five models all perform better than those trained solely by alloy composition, demonstrating the benefits of feature diversity. The benchmarking test indicates that the CNN model has higher accuracy, compared to the empirical equations, JMatPro software, and thermodynamic model. Besides, the simplified CNN models trained with remaining features after each stage of the ‘three-stage feature screening’ all show an error of only about 1 K from the original CNN model, illustrating the effectiveness of the current feature screening strategy and good robustness of the CNN model. Moreover, the CNN model can be utilized to predict the of the alloys with unknown compositional combinations, then new insights about the impacts of alloy elements on austenite stability and alloy design can be revealed. The integration of multi-scale data mining into a deep learning framework represented by CNN, offers a recipe for predicting certain attributes that are involved in complicated relationships with alloy composition.
{"title":"Predicting the martensite start temperature of steels via a combination of deep learning and multi-scale data mining","authors":"Shuai Wang, Xunwei Zuo, Nailu Chen, Yonghua Rong","doi":"10.1016/j.commatsci.2024.113430","DOIUrl":"10.1016/j.commatsci.2024.113430","url":null,"abstract":"<div><div>The martensite start (<span><math><mrow><msub><mi>M</mi><mi>s</mi></msub></mrow></math></span>) temperature, plays a significant role in guiding the alloy design and heat treatment process for steels. However, due to the complexity of martensite transformation, it remains challenging to establish more generalized models. In this study, the database with three scales was first built by multi-scale data mining, without relying on thermodynamic software. Then a convolutional neural network (CNN) model, as well as four traditional machine learning (ML) models, were trained to predict <span><math><mrow><msub><mi>M</mi><mi>s</mi></msub></mrow></math></span> using multi-scale database. The CNN model exhibits the smallest error, and the five models all perform better than those trained solely by alloy composition, demonstrating the benefits of feature diversity. The benchmarking test indicates that the CNN model has higher accuracy, compared to the empirical equations, JMatPro software, and thermodynamic model. Besides, the simplified CNN models trained with remaining features after each stage of the ‘three-stage feature screening’ all show an error of only about 1 K from the original CNN model, illustrating the effectiveness of the current feature screening strategy and good robustness of the CNN model. Moreover, the CNN model can be utilized to predict the <span><math><mrow><msub><mi>M</mi><mi>s</mi></msub></mrow></math></span> of the alloys with unknown compositional combinations, then new insights about the impacts of alloy elements on austenite stability and alloy design can be revealed. The integration of multi-scale data mining into a deep learning framework represented by CNN, offers a recipe for predicting certain attributes that are involved in complicated relationships with alloy composition.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"246 ","pages":"Article 113430"},"PeriodicalIF":3.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424697","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号