Computational Materials Science最新文献

{"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}

{"title":"A universal ML model for segregation in W","authors":"Christoph Dösinger ,&nbsp;Oleg E. Peil ,&nbsp;Daniel Scheiber ,&nbsp;Lorenz Romaner","doi":"10.1016/j.commatsci.2025.113847","DOIUrl":"10.1016/j.commatsci.2025.113847","url":null,"abstract":"<div><div>Segregation of solute elements to grain-boundaries (GB) in alloys is a key process controlling material properties. Examples are phase transformations, strength, or nanocrystalline stability. The central quantities to predict GB segregation are the site-specific segregation energies which can be accurately calculated using density functional theory (DFT). To reduce the computational cost, machine learning (ML) models are trained on DFT segregation data to predict the segregation energies. Here, we combine descriptors for the local structure of the segregation site with element-specific parameters for the solute element to train ML models that can predict the site-specific segregation energies for a wide range of elements. We use cross-validation and extrapolation scores to find the optimal set of descriptors for the model. The thus obtained model is then used to predict the segregation energies of solutes that are not in the data set. We apply our approach to segregation of transition metals in W. Both, cross-validation scores and comparison to literature data highlight excellent results of the ML approach. We make the model available by publishing the relevant codes and data.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113847"},"PeriodicalIF":3.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726148","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":"Dislocation properties in BCC refractory compositionally complex alloys from atomistic simulations","authors":"Juntan Li,&nbsp;Haixuan Xu","doi":"10.1016/j.commatsci.2025.113859","DOIUrl":"10.1016/j.commatsci.2025.113859","url":null,"abstract":"<div><div>Body-centered cubic (BCC) refractory compositionally complex alloys (RCCAs) have emerged as promising candidates for aerospace, nuclear energy, and automotive applications due to their exceptional high-temperature strengths. It is well-known that dislocations play a critical role in the mechanical properties of refractory alloys. In this study, we examine the fundamental properties of edge and screw dislocations, including core energies and dislocation shear stresses (DSSs) in MoNbTi, NbMoTaW, and CrTaVW, at various temperatures using atomistic simulations with the state-of-the-art machine-learned interatomic potentials (MLIPs). Our findings reveal that at high temperatures, the DSS of edge dislocations exceed those of screw dislocations in MoNbTi and NbMoTaW alloys. This behavior is attributed to cross-kink diffusion and annihilation in screw dislocations, which leads to a more significant decrease in DSS as temperature increases. Furthermore, the DSS values of screw dislocations at low temperatures and those of edge dislocations at high temperatures closely align with experimental yield strengths. These results show that edge dislocations are primarily responsible for the high-temperature strengths of some of the RCCAs and are crucial for tuning their mechanical properties. Additionally, we observe that screw dislocations exhibit lower core energies than edge dislocations across all temperatures in the investigated alloys, indicating their greater thermodynamic stability. These findings underscore the importance of considering different types of dislocations at various temperature regimes in BCC RCCAs, which is essential for guiding alloy design within the vast compositional space.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113859"},"PeriodicalIF":3.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715962","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":"Large language model-driven database for thermoelectric materials","authors":"Suman Itani ,&nbsp;Yibo Zhang ,&nbsp;Jiadong Zang","doi":"10.1016/j.commatsci.2025.113855","DOIUrl":"10.1016/j.commatsci.2025.113855","url":null,"abstract":"<div><div>Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"253 ","pages":"Article 113855"},"PeriodicalIF":3.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716074","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}