Computational Materials Science最新文献

{"title":"Molybdenum segregation at grain boundaries in a nanograined Ni-Mo alloy: Implications for yielding behavior and plastic deformation modes","authors":"Sihan Hao ,&nbsp;Jiaxiang Li ,&nbsp;Kenta Yamanaka ,&nbsp;Akihiko Chiba","doi":"10.1016/j.commatsci.2025.113973","DOIUrl":"10.1016/j.commatsci.2025.113973","url":null,"abstract":"<div><div>Solute segregation at grain boundaries (GBs) significantly modifies GB characteristics and influences the macroscopic properties of nanograined polycrystals. This study demonstrates a substantial impact of Mo segregation at GBs on the GB characteristics, yielding behavior, and plastic deformation modes in a nanograined Ni-Mo alloy. Atomic segregation simulations reveal that Mo atoms primarily occupy tensile stress sites at amorphous GBs without substantially altering site volume. However, Mo atoms at tensile stress sites compress atomic volumes at compressive stress sites, thereby increasing compressive stress. Consequently, overall GB atomic volume decreases while GB atomic compressive stress increases. Tensile deformation simulations indicate that dislocation emission from GBs is inhibited as the fraction of Mo atoms at GBs increases. The decreased GB energy and atomic volume, along with increased atomic compressive stress, are indicative of the inhibition of dislocation emission due to Mo segregation. When the excess Mo concentration reaches 2.9 at.%, nanograin boundary relaxation is induced, mitigating nanograin coarsening and softening.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113973"},"PeriodicalIF":3.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068969","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 Ni-based single crystal superalloys by machine learning based on data-driven multi-task optimization","authors":"Lixian Lian ,&nbsp;Wenjing Li ,&nbsp;Yu Zhang ,&nbsp;Xiufang Gong ,&nbsp;Wang Hu ,&nbsp;Ying Liu","doi":"10.1016/j.commatsci.2025.113969","DOIUrl":"10.1016/j.commatsci.2025.113969","url":null,"abstract":"<div><div>In order to ascertain the requirements of high-quality gas turbine components, an investigation was undertaken into the composition design of Ni-based single crystal superalloys utilising high-throughput, data-driven machine learning methodologies. A characteristic parameter space influencing high-temperature strength in superalloys was established through theoretical calculation and the extraction of data from the superalloy manual. By means of correlation screening, it was determined that Vγ′、V_TCP and Tγ′-solvus represent the crucial parameters, which were used as the basis for optimisation tasks. In accordance with the principles of domain knowledge, a series of constraints were established for the purpose of screening the prospective alloy components, with the objective of achieving a high level of temperature strength. Subsequently, the preferred composition alloys and the commercial alloys were prepared through experimentation. Characterisation of the new composition alloys revealed that they exhibited a microstructure with 60 ∼ 75 % cubic γ′ phase, which demonstrated a relatively strong correlation with the optimisation tasks. Furthermore, the results show that the new alloys exhibit not only superior high-temperature strength but also notable improvements in elongation, density and cost, conferring them with enhanced economic value and potential for wider application compared to the current commercial alloys. The correlation between microstructure and properties suggests that the enhancement in performance is attributable to the optimisation of key microstructure objectives, offering a promising avenue for alloy design.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113969"},"PeriodicalIF":3.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139720","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":"SimGate: A deep learning surrogate model for predicting microstructure evolution using the phase-field method","authors":"Pin Wu ,&nbsp;Haiwang Huang ,&nbsp;Qingcheng Yang ,&nbsp;Bo Qian ,&nbsp;Yongxin Gao ,&nbsp;Yiguo Yang ,&nbsp;Huiran Zhang ,&nbsp;Qiang Zhen","doi":"10.1016/j.commatsci.2025.113883","DOIUrl":"10.1016/j.commatsci.2025.113883","url":null,"abstract":"<div><div>This study introduces SimGate, a novel deep learning surrogate model for predicting microstructure evolution using the phase-field method. Combining the temporal modeling capabilities of “Simpler yet better video prediction (SimVP)” with the multi-order aggregation features of “Multi-order gated aggregation network (MogaNet)”, SimGate leverages robust temporal dynamics alongside spatial and channel aggregation modules to ensure precise detail capture and spatial consistency. To demonstrate SimGate’s ability to tackle challenging scenarios, high-temperature sintering simulations of polycrystalline cerium dioxide (CeO₂) particles were selected as a test case. These simulations, chosen for their complexity, involve both Cahn–Hilliard-type and Allen–Cahn-type phase-field equations along with intricate interfacial dynamics, and they were validated through experimental data. SimGate accurately predicts the sintering process from limited initial time steps and exhibits strong extrapolation capabilities in modeling unseen microstructures over extended time scales. Compared to traditional phase-field simulations, which require hours per case, SimGate reduces computational time to seconds while maintaining a prediction accuracy of around 90%. Additionally, point-wise error analysis shows that the average accuracy is improved by 7.80% and 12.41% compared with the original SimVP and well-known Long Short-Term Memory Networks (LSTM), respectively. An ablation analysis was performed to reveal the contributions of key components in the proposed SimGate framework. By significantly enhancing computational efficiency and accuracy, SimGate demonstrates broad potential as a generalizable microstructure prediction model applicable to diverse material and mechanical processing scenarios beyond sintering.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113883"},"PeriodicalIF":3.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937105","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":"Integrated computational framework for the optimization of the microstructure in additive manufacturing of metals","authors":"Victor D. Fachinotti ,&nbsp;Sylvain Gouttebrozze ,&nbsp;Stéphane Dumoulin ,&nbsp;Xiaobo Ren","doi":"10.1016/j.commatsci.2025.113944","DOIUrl":"10.1016/j.commatsci.2025.113944","url":null,"abstract":"<div><div>We present an integral multiscale computational framework for the optimization of the microstructure in metal additive manufacturing. It consists of four modules: (i) the optimization solver that systematically generates feasible designs, (ii) the macroscale module for determining temperature evolution along the deposition process and further cooling, (iii) the microscale module that computes the evolution of microstructure in the deposited part for the temperature histories computed in the previous step, and (iv) the assessment module that quantifies how good the microstructure of the as-deposited part is for each design. The macroscale module uses ABAQUS for the finite element analysis of nonlinear transient heat transfer. The microscale module is a fast metamodel of the multiphase-field model for multicomponent alloys from the microstructure simulation software MICRESS. This metamodel is based on the Johnson–Mehl–Avrami–Kolmogorov law for isothermal transformations, calibrated using MICRESS’ results, and extended to non-isothermal transformations by approximating them as a series of isothermal steps. The whole workflow is implemented into ISIGHT, a user-friendly software that provides a suite of visual tools to create simulation process flows. Finally, the laser directed energy deposition of duplex stainless steels, whose mechanical properties are highly dependent on the ferrite–austenite ratio, is taken as case study. Results show that the microstructure of the as-deposited part can be significantly improved.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113944"},"PeriodicalIF":3.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937077","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":"High-pressure stability and mechanical properties of manganese nitrides: A DFT study","authors":"Assyl-Dastan B. Bazarbek ,&nbsp;Nursultan E. Sagatov ,&nbsp;Aitolkyn S. Omarkhan ,&nbsp;Dinara N. Sagatova ,&nbsp;Abdirash T. Akilbekov","doi":"10.1016/j.commatsci.2025.113948","DOIUrl":"10.1016/j.commatsci.2025.113948","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Based on the evolutionary algorithms and the density functional theory, an extensive search for the stable manganese–nitrogen compounds and their structures was conducted in the pressure range of 0–200 GPa. As a result, one new manganese nitride Mn&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;N&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; was predicted, and the stability fields of the manganese nitrides were determined. It was demonstrated that there are six stable compounds in the Mn–N system at a pressure up to 200 GPa, namely Mn&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;N, Mn&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;N, Mn&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;N&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, MnN, MnN&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, and MnN&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;. Mn&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;N is stable in the form of the &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;̄&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; structure up to 191 GPa without any structural phase transition, and above this pressure, it decomposes into the isochemical mixture. Mn&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;N has one stable modification &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, which remains its stability in the entire considered pressure range. Previously unknown nitride Mn&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;N&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; stabilizes above 180 GPa in the &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; structure. MnN&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; stabilizes above 8 GPa and has three stable modifications &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mover&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;̄&lt;/mo&gt;&lt;/mrow&gt;&lt;/mover&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. MnN&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;-&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, which has a narrow stability pressure range (145–147 GPa), was shown to be metastable when taking into account the zero-point energy contribution and the temperature effect. MnN&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is ","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113948"},"PeriodicalIF":3.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937104","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":"Small-cell-based fast active learning of machine learning interatomic potentials","authors":"Zijian Meng ,&nbsp;Hao Sun ,&nbsp;Edmanuel Torres ,&nbsp;Christopher Maxwell ,&nbsp;Ryan Eric Grant ,&nbsp;Laurent Karim Béland","doi":"10.1016/j.commatsci.2025.113919","DOIUrl":"10.1016/j.commatsci.2025.113919","url":null,"abstract":"<div><div>Machine learning interatomic potentials (MLIPs) are often trained with on-the-fly active learning, where sampled configurations from atomistic simulations are added to the training set. However, this approach is limited by the high computational cost of <em>ab initio</em> calculations for large systems. Recent works have shown that MLIPs trained on small cells (1–8 atoms) rival the accuracy of large-cell models (100s of atoms) at far lower computational cost. Herein, we refer to these as small-cell and large-cell training, respectively. In this work, we iterate on earlier small-cell training approaches and characterize our resultant small-cell protocol. Potassium and sodium-potassium systems were studied: the former, a simpler system benchmarked in detail; the latter, a more complex binary system for further validation. Our small-cell training approach achieves up to two orders of magnitude of cost savings compared to large-cell (54-atom) training, with some training runs requiring fewer than 120 core-hours. Static and thermodynamic properties predicted using the MLIPs were evaluated, with small-cell training in both systems yielding strong <em>ab initio</em> agreement. Small cells appear to encode the necessary information to model complex large-scale phenomena—solid-liquid interfaces, critical exponents, diverse concentrations—even when the training cells themselves are too small to accommodate these phenomena. Based on these tests, we provide analysis and recommendations.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113919"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932115","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":"Fracture behavior of ordered and disordered solids predicted by atomistic simulations","authors":"Zhimin Chen ,&nbsp;Tao Du ,&nbsp;Morten M. Smedskjaer","doi":"10.1016/j.commatsci.2025.113956","DOIUrl":"10.1016/j.commatsci.2025.113956","url":null,"abstract":"<div><div>Crack initiation and propagation start at the atomic level but can lead to material failure. The mechanical response of a solid, brittle or ductile, therefore depends on the type of bonding and degree of order and disorder. However, from an engineering perspective, predicting the stress–strain response of various solid materials remains highly challenging. Building on molecular dynamics simulations, we here investigate these phenomena at the atomic scale in both ordered (crystalline) and disordered (glassy) solids with bonding types covering covalent, metallic, ionic, coordination, and hydrogen bonding. We demonstrate that stress accumulation and release are inherently tied to the change in the atomic volumes of the atoms in both the ordered and disordered solids. Based on this, we propose a universal model for predicting the microscopic fracture behavior. Specifically, the stress–strain response can be predicted by the loading-induced atomic volume change combined with an energy-related constant that is related to the bonding type. The model is applicable to a wide range of solid materials, and thus elucidates the intrinsic relation between the mechanical behavior and atomic-scale features, offering a new tool for atomistic design of strong and tough solid materials.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113956"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922024","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":"Improved crystal graph networks with periodic invariance from a global perspective","authors":"Zhihui Wang ,&nbsp;Chengyu Fu ,&nbsp;Chuchu Xu ,&nbsp;Huaijuan Zang ,&nbsp;Jing Fang ,&nbsp;Yongsheng Ren ,&nbsp;Shu Zhan","doi":"10.1016/j.commatsci.2025.113951","DOIUrl":"10.1016/j.commatsci.2025.113951","url":null,"abstract":"<div><div>In recent decades, the swift progress of machine learning has significantly propelled the advancement of crystal material property prediction and generation. Graph Neural Networks (GNN), as a key tool in AI for Science research, play a crucial role in processing data from various scientific problems. However, most existing GNN models focus on improving the accuracy of crystal material property predictions by exploring the crystal’s geometric structure and altering deep learning model approaches, while neglecting the ratio information of elemental composition, which represents the global information of the crystal. In this study, we propose a new model for crystal material property prediction, Gformer. Gformer combines periodic pattern encoding with self-connecting edges, graph attention convolution, and a global feature extraction and processing module. This allows it to capture repeated patterns in the crystal structure and the macroscopic effects of elemental composition on material properties. The model was trained and evaluated using the JARVIS-DFT and Materials Project databases. The results demonstrate that Gformer achieves outstanding performance in six crystal property prediction tasks. Our code is publicly available at <span><span>https://github.com/wwzhui/Gformer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113951"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917653","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":"Atomic-scale insights into rare earth Oxo-Cation stabilization in HY zeolites: A periotic DFT study","authors":"Minghui Shen ,&nbsp;Huimin Guan ,&nbsp;Qiang Li ,&nbsp;Li Zhang ,&nbsp;Shengyu Se ,&nbsp;Xiaofeng Du ,&nbsp;Yucai Qin ,&nbsp;Lijuan Song","doi":"10.1016/j.commatsci.2025.113955","DOIUrl":"10.1016/j.commatsci.2025.113955","url":null,"abstract":"<div><div>This study systematically investigates the stabilization mechanisms and structural-electronic modulation of rare earth cations (La³⁺, Ce³⁺, Y³⁺) at distinct crystallographic sites (SI’, SII, SIII) in HY zeolites using density functional theory (DFT) calculations. It is revealed that the stability of rare earth oxo-cations (REO⁺) strongly depends on their occupied positions and ionic radii. At the SI’ sites, LaO⁺, with a larger ionic radius (1.16 Å), exhibits the lowest formation energy (ΔE &lt; 0) and preferentially stabilizes within the 6-membered rings (6-MR) of sodalite cages through strong interactions with framework oxygen atoms, while Y³⁺ (0.90 Å) induces localized lattice distortions and migrates to SII/SIII sites due to spatial constraints. Structural analyses demonstrate that REO⁺ incorporation synergistically regulates zeolite stability via geometric effects (e.g., 6-MR contraction and supercage expansion) and electronic effects (weakened Al-O bond polarization and enhanced RE-O charge transfer). Specifically, La³⁺ strengthens covalent bonding through d-orbital-mediated directional charge transfer, whereas Ce³⁺ induces asymmetric charge redistribution via 4f-orbital participation, which is also proofed by the COHP and DOS analysis. This work elucidates the atomic-scale site selectivity and stabilization mechanisms of RE cations in zeolites, providing theoretical insights for designing highly stable RE-modified HY zeolite catalysts with tailored acidity. These findings hold significant implications for industrial applications such as petroleum cracking and environmental catalysis.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113955"},"PeriodicalIF":3.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912213","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":"From SMILES to scattering: Automated high-throughput atomistic polyurethane simulations compared with WAXS data","authors":"Dominic Robe ,&nbsp;Adrian Menzel ,&nbsp;Andrew W. Phillips ,&nbsp;Elnaz Hajizadeh","doi":"10.1016/j.commatsci.2025.113931","DOIUrl":"10.1016/j.commatsci.2025.113931","url":null,"abstract":"<div><div>A critical bottleneck in high throughput molecular modeling is the manual declaration of force field parameters. An expert operator must consider the particular environment of each atom to specify its interactions. We address this challenge by developing an end-to-end fully automated workflow, which integrates and extends several software tools (LAMMPS, RDKit, RadonPy, Signac, Psi4, and Freud) to construct, execute, and analyze molecular dynamics simulations of polymers en masse without any operator. We study polyurethanes as a class of materials with a non-trivial multi-block structure and a wide range of achievable properties. Our workflow receives SMILES strings representing hard, soft, and chain extender monomers, and procedurally constructs fully specified models with varied chemistry, molecular weight, and hard component volume fraction. This automatic modeling of polyurethanes required novel implementation of explicit representations of full chemical structures, as well as neighborhood-dependent atomic charges. With these considerations, automatically constructed models reproduced the experimental structure data from WAXS experiments, in spite of model assumptions and computational limitations. Simulations with varying hard segment content indicate that the structure factor interpolates linearly between the extremes of nearly pure hard or soft systems. The effects of temperature, block length, and block connectivity are also investigated systematically. This capability enables fully autonomous high-throughput expansion of computational data sets necessary for machine learning, material screening, and inverse design.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113931"},"PeriodicalIF":3.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912214","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}