Computational Materials Science最新文献

{"title":"Stochastic scaling of the time step length in a full-scale Monte Carlo Potts model","authors":"Sang-Ho Oh ,&nbsp;Chan Lim ,&nbsp;Byeong-Joo Lee","doi":"10.1016/j.commatsci.2024.113644","DOIUrl":"10.1016/j.commatsci.2024.113644","url":null,"abstract":"<div><div>Grain growth is a fundamental reaction in polycrystalline solid materials that significantly influences various material properties. The Monte Carlo Potts model is notable for its simple algorithm and low computational cost, effectively capturing fundamental grain growth kinetics and providing quantitative predictions. Its realistic time assignment scheme relies on experimental information and simulation resolution. This can result in excessively short simulation time step, significantly increasing the required simulation time and limiting its practical use in real-world materials design. Here, we propose a novel scheme to control the actual length of a Monte Carlo step based on the fundamental physical principles underlying the Monte Carlo algorithm. The simulation results were confirmed to be consistent after the timescale adjustment. Furthermore, the present approach provides a reasonable way to determine the length of a Monte Carlo step in complex simulations where multiple Monte Carlo Potts models for different reaction kinetics are included. The present approach is expected to broaden the applicability of the Monte Carlo Potts model for practical processes in the real world.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113644"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151126","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":"Accelerating active learning materials discovery with FAIR data and workflows: A case study for alloy melting temperatures","authors":"Mohnish Harwani ,&nbsp;Juan C. Verduzco ,&nbsp;Brian H. Lee ,&nbsp;Alejandro Strachan","doi":"10.1016/j.commatsci.2024.113640","DOIUrl":"10.1016/j.commatsci.2024.113640","url":null,"abstract":"<div><div>Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, a major challenge remains the acquisition of the initial data and the development of workflows to generate new data at each iteration. In this study, we demonstrate a significant speedup in an optimization task by reusing a published simulation workflow available for online simulations and its associated data repository, where the results of each workflow run are automatically stored. Both the workflow and its data follow FAIR (findable, accessible, interoperable, and reusable) principles using nanoHUB’s infrastructure. The workflow employs molecular dynamics to calculate the melting temperature of multi-principal component alloys. We leveraged all prior data not only to develop an accurate machine learning model to start the sequential optimization but also to optimize the simulation parameters and accelerate convergence. Prior work showed that finding the alloy composition with the highest melting temperature required testing several alloy compositions, and establishing the melting temperature for each composition took, on average, multiple simulations. By developing a workflow that utilizes the FAIR data in the nanoHUB database, we reduced the number of simulations per composition to one and found the alloy with the lowest melting temperature testing only three compositions. This second optimization, therefore, shows a speedup of 10x as compared to models that do not access the FAIR databases.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113640"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151132","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":"Solidification of a quaternary X5CrNi18-10 alloy during laser beam welding using CALPHAD data in a phase-field approach","authors":"Muhammad Umar ,&nbsp;Marco Seiz ,&nbsp;Michael Kellner ,&nbsp;Britta Nestler ,&nbsp;Daniel Schneider","doi":"10.1016/j.commatsci.2024.113627","DOIUrl":"10.1016/j.commatsci.2024.113627","url":null,"abstract":"<div><div>Dendritic growth is a common phenomenon during the solidification of alloys, and it has a significant impact on the final microstructure and mechanical properties of the material. This research study investigates the solidification behaviour of quaternary X5CrNi18-10 alloys at thermochemical conditions similar to the laser beam welding (LBW) process. The aim of this investigation is to gain a comprehensive understanding of microstructure evolution at the microscale and their correlation with the macroscopic welding process conditions. To achieve this, a combined approach using the CALculation of PHAse Diagrams (CALPHAD) database and phase-field simulations is employed. Based on the CALPHAD-derived Gibbs energy functions, phase-field simulations are performed to simulate the solidification with dendritic/cellular morphology. The study focuses on solidification microstructure evolution influenced by process conditions such as thermal gradient and LBW velocity at steady-state conditions. By analysing the solidification microstructure morphology in 2D, valuable insights into the solidification kinetics and the influence of local thermal conditions on dendritic growth are obtained. Furthermore, the micro-segregation behaviour of key alloying elements during solidification in the mushy zone is explored. This study will help to enhance the understanding of dendritic solidification in this welding process, facilitating the optimisation of process parameters for improved mechanical properties.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113627"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151778","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":"Specific surface area (SSA) of perovskites with uncertainty estimation approach","authors":"Zied Hosni ,&nbsp;Sofiene Achour ,&nbsp;Fatma Saadi ,&nbsp;Jingru Lin ,&nbsp;Jie Sheng ,&nbsp;Mohammed Al Qaraghuli","doi":"10.1016/j.commatsci.2025.113668","DOIUrl":"10.1016/j.commatsci.2025.113668","url":null,"abstract":"<div><div>In recent years, the rapid development of computer technology and the Internet has enabled faster and more convenient data acquisition. Using big data and machine learning (ML) to accelerate the development of new materials is crucial. This study provides insight into the application of ML in predicting the specific surface area (SSA) of ABO₃-type perovskite. In this article, we initially introduce the basic concepts of ML, its general workflow in materials research, and the specific algorithms used. We further discuss the structural features of ABO₃-type perovskites and their development trends in photocatalytic applications. The prediction of SSA is vital for the application and performance optimization of perovskites, and ML provides an efficient and accurate means of prediction. In the modeling process, we collected experimental data on 50 ABO₃-type perovskites from published literature and feature-engineered them in various ways to obtain three different datasets. By screening the features through principal component analysis (PCA) and Genetic Algorithm (GA), we found that GA is superior to the Random Forest (RF) model. At the same time, PCA is more suitable for the Support Vector Regression (SVR) model. The test results showed that the RF model has a prediction accuracy of 0.832, while the SVR model has a prediction accuracy of 0.808, and both methods exhibited high prediction accuracy. In addition, the leave-one-out cross-validation results further confirmed the robustness of both models. Finally, we conducted an in-depth analysis of the importance of features. The results showed that the calcination temperature (CT) and calcination time (AH) are the most critical features, which are positively and negatively correlated with the SSA of perovskite oxides, respectively. This provides valuable reference information for the design and optimization of perovskite materials. Overall, this study successfully predicted the SSA of ABO₃-type perovskites using an ML method, which provides a new and effective tool for researching and applying perovskite materials.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113668"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151780","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":"ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification","authors":"Matt Rolchigo,&nbsp;Samuel Temple Reeve,&nbsp;Benjamin Stump,&nbsp;John Coleman,&nbsp;Alex Plotkowski","doi":"10.1016/j.commatsci.2025.113734","DOIUrl":"10.1016/j.commatsci.2025.113734","url":null,"abstract":"<div><div>The previously established ExaCA software for performance portable alloy grain structure simulation has been updated to better represent the solidification behavior during complex alloy processing conditions, such as those encountered during metal additive manufacturing (AM), and for improved performance and scalability. An extension to the time–temperature history input data format and the core ExaCA algorithm to include an arbitrary number of melting and solidification events yielded improved prediction of texture for various melt pool geometries, expanding the range of AM-relevant conditions that can be accurately simulated. Improved heat transport process simulation coupling, including the creation of large raster datasets from single track time–temperature history data and in-memory coupling with the new, performance portable finite difference code Finch, were also demonstrated in example studies on the effect of multilayer AM microstructure predictions on hatch spacing and cell size, respectively. Additional new features are detailed and demonstrated, including the ability to perform simulations using various interfacial response function forms, execute simulations on state-of-the-art hardware, improved usability through post-processing versatility, and improved strong and weak scaling performance. The performance, physics, and versatility improvements demonstrated here will further enable large-scale studies on AM process–microstructure relationships that were not previously possible. Furthermore, the usability improvements and ability to run coupled AM process–microstructure simulations using the Finch-ExaCA workflow will facilitate broader use of this open-source software by the computational materials community.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"251 ","pages":"Article 113734"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143352662","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":"Virtual texture analysis to understand microstructure effects on deformation twinning and detwinning behavior in BCC metals","authors":"Aadhithyan Kannan,&nbsp;Phillip Tsurkan,&nbsp;Avinash M. Dongare","doi":"10.1016/j.commatsci.2024.113636","DOIUrl":"10.1016/j.commatsci.2024.113636","url":null,"abstract":"<div><div>Understanding and predicting deformation twinning contributions to plastic deformation in BCC metals has been a long-standing challenge due to the interplay with dislocation slip and non-Schmid effects that render an asymmetry under tension and compression. This paper uses molecular dynamics simulations to understand the effect of unloading and grain orientation on deformation twinning in a nanocrystalline Fe (nc-Fe) system as a model BCC metal. The nc-Fe system is loaded under uniaxial stress tension and compression to understand the effect of grain orientation (Schmid effects) on deformation twinning behavior and the tension/compression asymmetry (non-Schmid effects). A new virtual texture analysis “VirTex” tool is used to understand the role of grain orientation on the nucleation of twins and their contributions to the observed stress–strain response. For certain grain orientations, the twinnability is observed to be different in tension and compression. In addition, the flow stress accommodation from twins in certain grains is observed to be different in tension and compression and different from that for the grains. Subsequent unloading leads to detwinning in the deformed microstructures, where the extent of detwinning depends on the strain from which the system is unloaded and on the morphology of the twin. Finally, the simulations are carried out to analyze the role of the Schmid factor on the twinnability and asymmetry in tension and compression.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113636"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150873","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 hybrid knowledge-guided and data-driven method for predicting low-alloy steels performance","authors":"Chuanjiang Qi ,&nbsp;Chengmeng Wang ,&nbsp;Dongmei Fu ,&nbsp;Lizhen Shao ,&nbsp;Ke Zhou ,&nbsp;Zhiyi Zhao","doi":"10.1016/j.commatsci.2024.113602","DOIUrl":"10.1016/j.commatsci.2024.113602","url":null,"abstract":"<div><div>Low-alloy steels are essential engineering materials and precise prediction of their performance is crucial. Data acquisition in the steel industry is costly, and the available data volume is usually small. So far, with limited data, both traditional material modeling methods and data-driven machine learning cannot accurately build the relationship among material composition, process and performance. Although there is a wealth of experience and knowledge accumulated through extensive research in the steel field, combining the knowledge with data remains a significant challenge. To fully utilize domain knowledge, in this paper, a knowledge-driven graph convolutional network (KD-GCN) method is proposed for predicting the performance of low-alloy steels. Firstly, a knowledge graph is constructed by using steel knowledge from professional books. Then structured steel composition and processing data are transformed into a graph representation. Next, through a multi-layer graph convolutional network, the domain knowledge and structured data are fused to predict the strength and plasticity of steels. Furthermore, the proposed method is tested on a low-alloy steel dataset. Comparison results demonstrate that KD-GCN outperforms some other machine learning methods without using domain knowledge. Finally, feature importance analysis experiments show that the obtained influence of composition and process on steel performance is highly consistent with domain knowledge, which further validates the effectiveness of the proposed method.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113602"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150874","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":"Coarse-grained modeling and multi-properties simulation of amorphous polyethylene","authors":"Xueming Yang ,&nbsp;Tianfu Yu ,&nbsp;Xiaozhong Zhang ,&nbsp;Yongfu Ma ,&nbsp;Jianfei Xie","doi":"10.1016/j.commatsci.2025.113670","DOIUrl":"10.1016/j.commatsci.2025.113670","url":null,"abstract":"<div><div>In this paper, a coarse-grained (CG) model of amorphous polyethylene (PE) has been developed, and the potential parameters of CG have also been derived. Accordingly, the multi-properties of amorphous PE such as its density, glass transition temperature (<em>T</em>_g), mechanical and thermal properties are studied by using Coarse-Grained Molecular Dynamics (CGMD) simulations. The potential parameters of CG model have been derived via an iterative Boltzmann inversion (IBI) method, and the bond lengths, bond angles and pair distributions obtained in CGMD simulations match well with those obtained in all-atom molecular dynamics (AAMD) simulations. The obtained potential parameters can be utilized to accurately reproduce the density, <em>T</em>_g and mechanical properties of amorphous PE, but the thermal conductivity returned by the CG model is only 1/2 of the actual value. Furthermore, the reasons for the lower thermal conductivity are discussed.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113670"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151707","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":"Thermally induced evolution in non-hydrogenated and hydrogenated amorphous carbon films: A molecular dynamics research","authors":"Shusen Guo ,&nbsp;Zhaoxi Zhao ,&nbsp;Jie Lv ,&nbsp;Suhua Xiao ,&nbsp;Yongshun Luo","doi":"10.1016/j.commatsci.2025.113661","DOIUrl":"10.1016/j.commatsci.2025.113661","url":null,"abstract":"<div><div>Amorphous carbons have received significant attention due to their excellent tribological properties. In this paper, molecular dynamics (MD) simulations were performed to study the thermally induced structural evolutions and hybridization transitions in non-hydrogenated (a-C) and hydrogenated (a-C:H) amorphous carbon films. The results indicated that the amorphous carbon gradually transforms into a graphitic carbon network under thermal effect. The hybridization transition is characterized by a three-stage process: (i) elongation of the existing bonds, (ii) breakage of one existing bond or formation of one bond, and (iii) relaxation. For low-density a-C film, the formation of void defects can be observed within high-temperature regions. With increasing densities, the diffusivity of a-C films decreases significantly, indicative of better thermal stability. Also, a glass-transition process can be observed in a-C films at around 3200 K, showing no obvious correlation with film densities. For a-C:H films, the incorporation of low-mass hydrogen atoms induces a highly diffusive amorphous network, which would deteriorate the thermal stability. With increasing hydrogen contents, both glass-transition temperatures and activation energies decrease. Also, as shown by the lower potential barriers and bond stretching distances, hybridization transitions are prone to occur in a-C:H films with high hydrogen contents.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113661"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151781","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":"Mechanism of highly selective adsorption behavior of gas molecules on CuO (110) surface","authors":"Yu Liu ,&nbsp;Wang-Ping Xu ,&nbsp;Zheng Liang ,&nbsp;Hua-Jian Tan ,&nbsp;Jue-Xian Cao ,&nbsp;Xiao-Lin Wei","doi":"10.1016/j.commatsci.2024.113618","DOIUrl":"10.1016/j.commatsci.2024.113618","url":null,"abstract":"<div><div>Exploring the interaction mechanisms between gas molecules and metal oxides can not only efficiently screen gate-sensitive materials to develop high-performance gas sensors but also effectively lower the energy barriers of catalytic reactions to improve catalytic efficiency. However, the detailed interaction mechanism and adsorption criterion between gas molecules and metal oxides is still unclear. Therefore, we systematically studied the adsorption behavior of NO, O₂, SO₂, H₂, NH₃, and CH₄ on the CuO(110) surface by first-principles. The results demonstrate that the CuO(110) performs excellent selective adsorption for NH₃ and NO. Notably, the HOMO orbitals of NH₃ and NO and the <em>dz²</em> orbitals of Cu atoms must have an energy level match. Furthermore, the <strong><em>σ</em></strong> bond of NH₃ interaction with the <em>dz²</em> orbitals of Cu atoms by “head-to-head” satisfied the parity match and resulted in the maximum overlap of orbital wave functions. Though the <strong><em>π</em></strong> bond of NO and the <em>dz²</em> orbitals of Cu atoms have a “head-to-corner” interaction, which also meets the maximum overlap of orbital wave functions due to the exposed orbitals of Cu atoms of the CuO(110) surface are mainly <em>dz²</em> orbitals. In addition, NH₃ exhibited outstanding recovery time on the CuO(110) surface from 450 K to 650 K temperatures. Our results provide an adsorption criterion for designing gate metal oxide-sensitive materials for gas sensors and catalytic reactions.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"249 ","pages":"Article 113618"},"PeriodicalIF":3.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150872","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}