Computational Materials Science最新文献

{"title":"A review on all-atom force fields capabilities to predict polymer properties: Case of poly(methyl methacrylate) and polyisobutylene polymer systems","authors":"R.L. Nkepsu Mbitou,&nbsp;F. Bedoui","doi":"10.1016/j.commatsci.2025.113861","DOIUrl":"10.1016/j.commatsci.2025.113861","url":null,"abstract":"<div><div>The strategy of this review is to list and discuss the most commonly used Class I and Class II atomistic force fields. The corresponding force-field parameters are detailed, and the functional form difference is mentioned in terms of each generation of the force field. The validity of each force field was checked by comparing the simulated properties values of two polymer test cases, with their experimental values. It was observed that Class II force fields are more convenient for predicting the thermomechanical properties of amorphous polymer systems, and they could be good candidates for molecular simulations of polymers reinforced by nanoparticles.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113861"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760437","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":"Magnetic phase transitions in (1−x)BiFeO3–xPbFe1/2Sb1/2O3 solid solutions studied by the Monte Carlo method","authors":"A.V. Motseyko ,&nbsp;A.V. Pushkarev ,&nbsp;N.M. Olekhnovich ,&nbsp;Y.V. Radyush ,&nbsp;N.V. Ter-Oganessian","doi":"10.1016/j.commatsci.2025.113860","DOIUrl":"10.1016/j.commatsci.2025.113860","url":null,"abstract":"<div><div>Bismuth ferrite (BiFeO<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, BFO) and its solid solutions are promising multiferroic materials with both magnetic and ferroelectric orderings. One such solid solution is <span><math><mrow><mo>(</mo><mn>1</mn><mo>−</mo><mi>x</mi><mo>)</mo></mrow></math></span>BiFeO<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> – <span><math><mi>x</mi></math></span>PbFe<span><math><msub><mrow></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msub></math></span>Sb<span><math><msub><mrow></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>. PbFe<span><math><msub><mrow></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msub></math></span>Sb<span><math><msub><mrow></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msub></math></span>O₃ (PFS) is also considered multiferroic, exhibiting a high peak in the dielectric constant at 190 K and electric polarization loops below this temperature. However, the magnetic properties and the ground state of PFS remain subjects of debate. The magnetic behaviour is arguably further complicated by its strong tendency to form cation-ordered structures. In this study, we investigate the magnetism in the <span><math><mrow><mo>(</mo><mn>1</mn><mo>−</mo><mi>x</mi><mo>)</mo></mrow></math></span>BiFeO<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> – <span><math><mi>x</mi></math></span>PbFe<span><math><msub><mrow></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msub></math></span>Sb<span><math><msub><mrow></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> solid solutions using the Monte Carlo method. The evolution of the magnetic phase transition temperature and of the type of magnetic ordering with varying PFS concentration is examined. We consider different scenarios of Fe<span><math><mo>−</mo></math></span>Sb atomic ordering: disordered, clustered, or with varying degrees of ordering, and build phase diagrams providing insights into the magnetism of these solid solutions.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113860"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760440","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":"Tensor equation of state for copper and aluminum","authors":"Boris A. Panchenko ,&nbsp;Eugenii V. Fomin ,&nbsp;Alexander E. Mayer","doi":"10.1016/j.commatsci.2025.113845","DOIUrl":"10.1016/j.commatsci.2025.113845","url":null,"abstract":"<div><div>Increasing the strain rate up to about 1/ns in the conditions of ultra-short-pulse powerful laser irradiation of thin metal foils of submicron thickness reveals very strong elastic precursors, for which decoupling of pressure and stress deviators is unreasonable. A nonlinear stress–strain relationship (tensor equation of state–TEOS) is required for establishing theoretical models of such dynamic processes and unsteady shock waves. We proposed an ANN-TEOS model, which adopts an artificial neural network (ANN) trained on the data of density functional theory (DFT) calculations for the cold curve and analytical form for thermal contributions fitted to molecular dynamics (MD) data. We showed the efficiency of feed-forward ANN in approximation of the cold curve. Besides, the range of applicability of Hooke’s law for approximation of the cold curve was examined. The DFT data were used to train a cold-curve ANN and to fit elastic constants of the Hooke’s law with nonlinear corrections for copper and aluminum. Whereas the ANN is applicable for a complex approximation within a wide range of deformed states, the Hooke’s law applicability is restricted to the strain level of about 0.1. MD simulations were used to construct and fit the thermal contributions. The developed ANN-TEOS model was applied to calculate the shock adiabats for both plastic shock wave (nearly hydrostatic omnidirectional loading) and elastic shock wave (uniaxial loading). Elastic shock Hugoniots lie above the plastic ones for both metals providing an opportunity for a shock wave to split into elastic precursor and plastic wave even for conditions, in which the plastic shock wave velocity exceeds the longitudinal sound speed. A simultaneous consideration of TEOS and plasticity model is required for the prediction of splitting and two-wave structure. Our comparison of several interatomic potentials with the stress sates calculated by means of DFT showed much higher precision of the classical force field in comparison with the examined machine-learning potentials for the considered problem of severe deformation. This result elucidates one more time the known problem of the restricted range of applicability of the machine-learning potentials and the need to include the required range of states in the training dataset for these potentials.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113845"},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761160","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":"Atomistic molecular dynamics simulations of the tensile strength properties of polymer-calcite systems","authors":"Keat Yung Hue ,&nbsp;Daniela Andrade Damasceno ,&nbsp;Myo Thant Maung Maung ,&nbsp;Paul F. Luckham ,&nbsp;Omar K. Matar ,&nbsp;Erich A. Müller","doi":"10.1016/j.commatsci.2025.113866","DOIUrl":"10.1016/j.commatsci.2025.113866","url":null,"abstract":"<div><div>The production of solids can occur in poorly consolidated carbonate rock reservoirs, leading to equipment damage and environmental waste. This issue can be mitigated by injecting formation-strengthening chemicals, and the performance of these chemicals can be assessed in terms of their tensile strength and interfacial interaction with calcite, the main component of carbonate formations. This study aims to investigate the tensile deformation behaviour of polymer-calcite systems. Classical atomistic molecular dynamics (MD) simulations are utilised to model the interaction of polyacrylamide-based polymer additives, including pure polyacrylamide (PAM), hydrolysed polyacrylamide (HPAM), and sulfonated polyacrylamide (SPAM) with a calcite (1 0 4) structure. Uniaxial tensile simulations demonstrate that the interfacial strength of the polymer-calcite system is significantly stronger than the corresponding bulk polymer strength, resulting in strong polymer adhesion at the calcite surface during deformation. HPAM exhibits high bulk polymer and interfacial strength, presumably due to the presence of the acrylate monomer in ionised form, making it an excellent formation-strengthening agent.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113866"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747368","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":"Atomistic simulations of short-range ordering with light interstitials in Inconel superalloys","authors":"Tyler D. Doležal ,&nbsp;Emre Tekoglu ,&nbsp;Jong-Soo Bae ,&nbsp;Gi-Dong Sim ,&nbsp;Rodrigo Freitas ,&nbsp;Ju Li","doi":"10.1016/j.commatsci.2025.113858","DOIUrl":"10.1016/j.commatsci.2025.113858","url":null,"abstract":"<div><div>This study employed hybrid Monte Carlo Molecular Dynamics simulations to investigate the short-range ordering behavior of Ni-based superalloys doped with boron or carbon. The simulations revealed that both boron and carbon dissociated from their host Ti atoms to achieve energetically favored ordering with Cr, Mo, and Nb. Boron clusters formed as B₂, surrounded by Mo, Nb, and Cr, while carbon preferentially clustered with Cr to form a Cr₂₃C₆ local motif and with Nb to form Nb₂C. Distinct preferences for interstitial sites were observed, with boron favoring tetrahedral sites and carbon occupying octahedral sites. In the presence of a vacancy, B₂ shifted from the tetrahedral site to the vacancy, where it remained coordinated with Mo, Nb, and Cr. Similarly, carbon utilized vacancies to form Nb₂C clusters. Excess energy calculations showed that B and C exhibited strong thermodynamic stability within their short-range ordered configurations. However, under Ti-rich conditions, C was more likely to segregate into TiC, despite preexisting ordering with Cr. This shift in stability suggests that increased Ti availability would alter carbide formation pathways, drawing C away from Cr-rich networks and promoting the development of TiC. Such redistribution may disrupt the continuity of Cr-based carbide networks, which play a critical role in stabilizing grain boundaries and impeding crack propagation. These effects further underscore the impact of interstitial-induced ordering on phase stability and microstructural evolution. This work provides an atomistic perspective on how boron- and carbon-induced ordering influences microstructure and mechanical properties. These findings highlight the critical role of interstitial-induced short-range ordering and demonstrate that this mechanism can be leveraged as a design principle to fine-tune alloy microstructures for specific engineering applications.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113858"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747371","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":"Controlling grain boundary mobility in phase-field-crystal model","authors":"Zhanxin Xiao ,&nbsp;Xin Su ,&nbsp;Dan Mordehai ,&nbsp;Nan Wang","doi":"10.1016/j.commatsci.2025.113869","DOIUrl":"10.1016/j.commatsci.2025.113869","url":null,"abstract":"<div><div>Grain boundary (GB) mobility is a key parameter in modelling microstructure evolution of polycrystalline materials. It is well known that GB mobility depends on the misorientation and possibly other degrees of freedom of the GB. This misorientation dependence has been calculated in numerous previous studies using molecular dynamics (MD) for several materials. However, MD simulations are computationally demanding due to need to account for atomic fluctuations, where the recently developed phase-field-crystal (PFC) method is shown to overcome this shortcoming. Nonetheless, GB mobility was not extensively studied using PFC, and it is not clear if the mobility in the PFC method has a similar misorientation dependency as the one extracted from the MD simulation. This work addresses this issue by calculating the GB mobility for several GBs in Nickel using both the MD simulation and the PFC. It is found that the misorientation dependent GB mobility in the PFC follow similar behavior as in the MD results when the kinetic factor is tuned to depend on the local-averaged density order parameter in the PFC model.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113869"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decision transformer approach to grain boundary network optimization","authors":"Christopher W. Adair,&nbsp;Oliver K. Johnson","doi":"10.1016/j.commatsci.2025.113852","DOIUrl":"10.1016/j.commatsci.2025.113852","url":null,"abstract":"<div><div>As microstructure property models improve, additional information from crystallographic degrees of freedom and grain boundary networks (GBNs) can be included in microstructure design problems. However, the high-dimensional nature of including this information precludes the use of many common optimization approaches and requires less efficient methods to generate quality designs. Previous work demonstrated that human-in-the-loop optimization, instantiated as a video game, achieved high-quality, efficient solutions to these design problems. However, such data is expensive to obtain. In the present work, we show how a Decision Transformer machine learning (ML) model can be used to learn from the optimization trajectories generated by human players, and subsequently solve materials design problems. We compare the ML optimization trajectories against players and a common global optimization algorithm: simulated annealing (SA). We find that the ML model exhibits a validation accuracy of 84% against player decisions, and achieves solutions of comparable quality to SA (92%), but does so using three orders of magnitude fewer iterations. We find that the ML model generalizes in important and surprising ways, including the ability to train using a simple constitutive structure–property model and then solve microstructure design problems for a different, higher-fidelity, constitutive structure–property model without any retraining. These results demonstrate the potential of Decision Transformer models for the solution of materials design problems.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113852"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747425","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 study on local site preference of interstitial oxygen in Ti3Zr1.5NbVAl0.25 high-entropy alloy","authors":"Ruili Liu ,&nbsp;Ruizhi Lu ,&nbsp;Aimin Wang ,&nbsp;Zhengwang Zhu ,&nbsp;Hao Wang","doi":"10.1016/j.commatsci.2025.113867","DOIUrl":"10.1016/j.commatsci.2025.113867","url":null,"abstract":"<div><div>The occupancy of interstitial oxygen atoms in high-entropy alloy exhibits site preferences, thus affecting alloy properties. In this work, first-principles calculations were employed to investigate the physical origin of the local site preference of oxygen in Ti₃Zr_1.5NbVAl_0.25 high-entropy alloy. The results indicate that the formation energies are closely correlated with the coordinating atoms in the interstitial environment. Interstitial oxygen tends to occupy the coordination environment of Ti and Zr, which is not conducive to stabilizing the Al coordination environment. Such local site preference primarily depends on the amount of charge transfer and lattice distortion, which encourages interstitial oxygen to occupy Ti and Zr-rich environments. Conversely, minimal charge transfer between Al and oxygen hinders the solid solution of interstitial oxygen. The present work thus offers insights and theoretical guidance for the design of high-performance lightweight refractory high-entropy alloys.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113867"},"PeriodicalIF":3.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747369","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":"Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential","authors":"Jianchuan Liu ,&nbsp;Tao Chen ,&nbsp;Sheng Mao ,&nbsp;Mohan Chen","doi":"10.1016/j.commatsci.2025.113825","DOIUrl":"10.1016/j.commatsci.2025.113825","url":null,"abstract":"<div><div>We develop a neural-network deep potential (DP) model spanning 0–3,000 K and 0–10 GPa, trained on density functional theory data across the full concentration Cu_xW_100-x compounds. We systematically investigate the structural and mechanical properties of W-Cu alloys. The results show that the bulk modulus (<em>B</em>) and Young’s modulus (<em>E</em>) of W-Cu alloys exhibit a linear decline as the Cu content increases, indicating a softening trend in the Cu_xW_100-x compounds as the Cu concentration rises. Besides, a brittle-to-ductile transition in the deformation mode predicted is predicted at around 37.5 at. % Cu content. Moreover, tensile testing demonstrates that Cu-poor region effectively block shear band advancement, simultaneously stimulating nucleation of secondary shear bands in adjacent Cu-rich domains. The results are anticipated to aid in exploring the physical mechanisms underlying the complex phenomena of W-Cu systems.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113825"},"PeriodicalIF":3.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734919","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":"Design of novel interpretable deep learning framework for microstructure–property relationships in nickel and cobalt based superalloys","authors":"Aditya Gollapalli,&nbsp;Abhishek Kumar Singh","doi":"10.1016/j.commatsci.2025.113854","DOIUrl":"10.1016/j.commatsci.2025.113854","url":null,"abstract":"<div><div>Featurization of microstructures is one of the most fundamental challenges in establishing microstructure–property relationships. Conventional machine learning and statistical methods require explicit featurization methods such as image processing, which are difficult to implement for complex and diverse sets of microstructures. To this end, deep learning methods such as convolution neural networks (CNNs) have been used to automate the featurization based on target properties. However, these CNNs do not include composition information limiting them to a single set of compositions. Moreover, these networks are complex and difficult to interpret. To overcome these challenges, a deep learning mixed input network consisting of a convolutional neural network (CNN) for microstructure input and an artificial neural network (ANN) for composition input is developed to predict the Vickers hardness of nickel and cobalt-based superalloys. A unique three-step optimization procedure is employed to reduce the complexity of the network. The network architecture is designed based on hardening models which allows the analysis of contributions of precipitation hardening and solid solution strengthening to the Vickers hardness. The network has been analyzed using synthetically generated controlled microstructures to understand the effect of microstructural features on the hardness. Furthermore, SHAPley additive explanations (SHAP) analysis has been used to understand the effect of composition and assess the interdependence between microstructure and composition in determining hardness.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113854"},"PeriodicalIF":3.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734820","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}