npj Computational Materials最新文献_第7页

EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys EFTGAN：用于高熵合金研究的元素特征和传输校正数据增强

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-01 DOI: 10.1038/s41524-025-01548-y

Yibo Sun, Cong Hou, Nguyen-Dung Tran, Yuhang Lu, Zimo Li, Ying Chen, Jun Ni

{"title":"EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys","authors":"Yibo Sun, Cong Hou, Nguyen-Dung Tran, Yuhang Lu, Zimo Li, Ying Chen, Jun Ni","doi":"10.1038/s41524-025-01548-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01548-y","url":null,"abstract":"Using machine learning to predict and design materials is an important mean of accelerating material development. One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors. However, the complexity of computing material structures limits the practical use of these models. To address this challenge and improve prediction accuracy in small data sets, we develop a generative network framework: Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks (EFTGAN). Combining the elemental convolution technique with Generative Adversarial Networks (GAN), EFTGAN provides a robust and efficient approach for generating data containing elemental and structural information that can be used not only for data augmentation to improve model accuracy, but also for prediction when the structures are unknown. Applying this framework to the FeNiCoCrMn/Pd high-entropy alloys, we successfully improve the prediction accuracy in a small data set and predict the concentration-dependent formation energies, lattices, and magnetic moments in quinary systems. This study provides a new algorithm to improve the performance and usability of deep learning with structures as inputs, which is effective and accurate for the prediction and development of materials for small data sets.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stacking-dependent electronic and topological properties in van der Waals antiferromagnet MnBi2Te4 films 范德华反铁磁体MnBi2Te4薄膜中堆叠相关的电子和拓扑性质

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-01 DOI: 10.1038/s41524-025-01545-1

Jiaheng Li, Quansheng Wu, Hongming Weng

{"title":"Stacking-dependent electronic and topological properties in van der Waals antiferromagnet MnBi2Te4 films","authors":"Jiaheng Li, Quansheng Wu, Hongming Weng","doi":"10.1038/s41524-025-01545-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01545-1","url":null,"abstract":"Combining first-principles calculations and tight-binding Hamiltonians, we study the stack-dependent behaviour of electronic and topological properties of layered antiferromagnet MnBi2Te4. Lateral shift of top septuple-layer greatly modifies electronic properties, and even induces topological phase transition between quantum anomalous Hall (QAH) insulators with C = 1 and trivial magnetic insulators with C = 0. The local energy minimum of “incorrect\" stacking order exhibits thickness-dependent topology opposite to the usual stacking order, which is attribute to relatively weakened interlayer Te-Te interaction in “incorrect\" stacking configuration. Our effective model analysis provides a comprehensive understanding of the underlying mechanisms involved, and we also propose two optical setups that can effectively differentiate between different stacking configurations. Our findings underscores the nuanced and profound influence that interlayer sliding in magnetic topological materials can have on the macroscopic quantum states, opening new avenues for the design and engineering of topological quantum materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Construction of a knowledge graph for framework material enabled by large language models and its application 基于大型语言模型的框架材料知识图谱的构建及其应用

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-27 DOI: 10.1038/s41524-025-01540-6

Xuefeng Bai, Song He, Yi Li, Yabo Xie, Xin Zhang, Wenli Du, Jian-Rong Li

{"title":"Construction of a knowledge graph for framework material enabled by large language models and its application","authors":"Xuefeng Bai, Song He, Yi Li, Yabo Xie, Xin Zhang, Wenli Du, Jian-Rong Li","doi":"10.1038/s41524-025-01540-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01540-6","url":null,"abstract":"Framework materials (FMs) have been extensively investigated with a plethora of literature documenting their unique properties and potential applications. Despite this, a comprehensive knowledge graph for this emerging field has not yet been constructed. In this study, by utilizing the natural language processing capabilities of large language models (LLMs), we have established a comprehensive knowledge graph (KG-FM). It covers synthesis, properties, applications, and other aspects of FMs including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs). The knowledge graph was constructed through the analysis of over 100,000 articles, resulting in 2.53 million nodes and 4.01 million relationships. Subsequently, its application has been explored for enhancing data retrieval, mining, and the development of sophisticated question-answering systems. Especially when integrating the KGs with LLMs, resulted Qwen2-KG not only achieves a higher accuracy rate of 91.67% in question-answering than existing models but also provides precise information sources.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised identification of crystal defects from atomistic potential descriptors 基于原子势描述符的晶体缺陷的无监督识别

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-27 DOI: 10.1038/s41524-025-01544-2

Lukáš Kývala, Pablo Montero de Hijes, Christoph Dellago

{"title":"Unsupervised identification of crystal defects from atomistic potential descriptors","authors":"Lukáš Kývala, Pablo Montero de Hijes, Christoph Dellago","doi":"10.1038/s41524-025-01544-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01544-2","url":null,"abstract":"Identifying crystal defects is vital for unraveling the origins of many physical phenomena. Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for long molecular dynamics simulations. Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations. We compare the performance of three such algorithms (PCA, UMAP, and PaCMAP) on silicon and water systems. Initially, we evaluate the algorithms for recognizing phases, including crystal polymorphs and the melt, followed by an extension of our analysis to identify interstitials, vacancies, and interfaces. While PCA is found unsuitable for effective classification, it has been shown to be a suitable initialization for UMAP and PaCMAP. Both UMAP and PaCMAP show promising results overall, with PaCMAP proving more robust in classification, except in cases of significant class imbalance, where UMAP performs better. Notably, both algorithms successfully identify nuclei in supercooled water, demonstrating their applicability to ice nucleation in water.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient equivariant model for machine learning interatomic potentials 机器学习原子间势的有效等变模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-26 DOI: 10.1038/s41524-025-01535-3

Ziduo Yang, Xian Wang, Yifan Li, Qiujie Lv, Calvin Yu-Chian Chen, Lei Shen

{"title":"Efficient equivariant model for machine learning interatomic potentials","authors":"Ziduo Yang, Xian Wang, Yifan Li, Qiujie Lv, Calvin Yu-Chian Chen, Lei Shen","doi":"10.1038/s41524-025-01535-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01535-3","url":null,"abstract":"In modern computational materials, machine learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional molecular dynamics (MD) simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, efficient models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. Here, we introduce an efficient equivariant graph neural network (E2GNN) that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals. Rather than relying on higher-order representations, E2GNN employs a scalar-vector dual representation to encode equivariant features. By learning geometric symmetry information, our model remains efficient while ensuring prediction accuracy and robustness through the equivariance. Our results show that E2GNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Furthermore, we conduct MD simulations using the E2GNN force field across solid, liquid, and gas systems. It is found that E2GNN can achieve the accuracy of ab initio MD across all examined systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ferroelectricity at the extreme thickness limit in the archetypal antiferroelectric PbZrO3 原型反铁电PbZrO3中厚度极限处的铁电性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-25 DOI: 10.1038/s41524-025-01520-w

Nikhilesh Maity, Milan Haddad, Nazanin Bassiri-Gharb, Amit Kumar, Lewys Jones, Sergey Lisenkov, Inna Ponomareva

{"title":"Ferroelectricity at the extreme thickness limit in the archetypal antiferroelectric PbZrO3","authors":"Nikhilesh Maity, Milan Haddad, Nazanin Bassiri-Gharb, Amit Kumar, Lewys Jones, Sergey Lisenkov, Inna Ponomareva","doi":"10.1038/s41524-025-01520-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01520-w","url":null,"abstract":"Size-driven transition of aNote, that the phasesn antiferroelectric into a polar ferroelectric or ferrielectric state is a strongly debated issue from both experimental and theoretical perspectives. While critical thickness limits for such transitions have been explored, a bottom-up approach in the ultrathin limit considering few atomic layers could provide insight into the mechanism of stabilization of the polar phases over the antipolar phase seen in bulk PbZrO3. Here, we use first-principles density functional theory to predict the stability of polar phases in Pt/PbZrO3/Pt nanocapacitors. In a few atomic layer thick slabs of PbZrO3 sandwiched between Pt electrodes, we find that the polar phase originating from the well established R3c phase of bulk PbZrO3 is energetically favorable over the antipolar phase originating from the Pbam phase of bulk PbZrO3. The famous triple-well potential of antiferroelectric PbZrO3 is modified in the nanocapacitor limit in such a way as to swap the positions of the global and local minima, stabilizing the polar phase relative to the antipolar one. The size effect is decomposed into the contributions from dimensionality reduction, surface charge screening, and interfacial relaxation, which reveals that it is the creation of well-compensated interfaces that stabilizes the polar phases over the antipolar ones in nanoscale PbZrO3.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"89 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient GPU-computing simulation platform JAX-CPFEM for differentiable crystal plasticity finite element method 高效gpu计算仿真平台JAX-CPFEM可微晶塑性有限元法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-22 DOI: 10.1038/s41524-025-01528-2

Fanglei Hu, Stephen Niezgoda, Tianju Xue, Jian Cao

{"title":"Efficient GPU-computing simulation platform JAX-CPFEM for differentiable crystal plasticity finite element method","authors":"Fanglei Hu, Stephen Niezgoda, Tianju Xue, Jian Cao","doi":"10.1038/s41524-025-01528-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01528-2","url":null,"abstract":"We present the formulation and applications of JAX-CPFEM, an open-source, GPU-accelerated, and differentiable 3-D crystal plasticity finite element method (CPFEM) software package. Leveraging the modern computing architecture JAX, JAX-CPFEM features high performance through array programming and GPU acceleration, achieving a 39× speedup in a polycrystal case with ~52,000 degrees of freedom compared to MOOSE with MPI (8 cores). Furthermore, JAX-CPFEM utilizes the automatic differentiation technique, enabling users to handle complex, non-linear constitutive materials laws without manually deriving the case-specific Jacobian matrix. Beyond solving forward problems, JAX-CPFEM demonstrates its potential in an inverse design pipeline, where initial crystallographic orientations of polycrystal copper are optimized to achieve targeted mechanical properties under deformations. The end-to-end differentiability of JAX-CPFEM allows automatic sensitivity calculations and high-dimensional inverse design using gradient-based optimization. The concept of differentiable JAX-CPFEM provides an affordable, flexible, and multi-purpose tool, advancing efficient and accessible computational tools for inverse design in smart manufacturing.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"50 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

First principles calculations of carrier dynamics of screw dislocation 螺旋位错载体动力学的第一性原理计算

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-21 DOI: 10.1038/s41524-025-01533-5

Qiang Gao, Zhengneng Zheng, Moshang Fan, Lin-Wang Wang

引用次数: 0

A physics-enforced neural network to predict polymer melt viscosity 预测聚合物熔体粘度的物理强化神经网络

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-20 DOI: 10.1038/s41524-025-01532-6

Ayush Jain, Rishi Gurnani, Arunkumar Rajan, H.Jerry Qi, Rampi Ramprasad

{"title":"A physics-enforced neural network to predict polymer melt viscosity","authors":"Ayush Jain, Rishi Gurnani, Arunkumar Rajan, H.Jerry Qi, Rampi Ramprasad","doi":"10.1038/s41524-025-01532-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01532-6","url":null,"abstract":"Achieving superior polymeric components through additive manufacturing (AM) relies on precise control of rheology. One rheological property particularly relevant to AM is melt viscosity (η). η is influenced by polymer chemistry, molecular weight (Mw), polydispersity, shear rate (({dot{gamma}})), and temperature (T). The relationship of η with Mw, ({dot{gamma }}), and T is captured by parameterized equations. Several physical experiments are required to fit the parameters, so predicting η of new polymer materials in unexplored physical domains is laborious. Here, we develop a Physics-Enforced Neural Network (PENN) model that predicts the empirical parameters and encodes the parametrized equations to calculate η as a function of polymer chemistry, Mw, polydispersity, ({dot{gamma }}), and T. We benchmark our PENN against physics-unaware Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) models. We demonstrate that the PENN offers superior values of η when extrapolating to unseen values of Mw, ({dot{gamma }}), and T for sparsely seen polymers.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The interplay between the martensitic transformation rate and the rate of plastic relaxation during martensitic transformation in low-carbon steel, a phase-field study 低碳钢马氏体相变过程中马氏体相变速率与塑性松弛速率的相互作用，进行了相场研究

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-20 DOI: 10.1038/s41524-024-01499-w

Hesham Salama, Oleg Shchyglo, Ingo Steinbach

{"title":"The interplay between the martensitic transformation rate and the rate of plastic relaxation during martensitic transformation in low-carbon steel, a phase-field study","authors":"Hesham Salama, Oleg Shchyglo, Ingo Steinbach","doi":"10.1038/s41524-024-01499-w","DOIUrl":"https://doi.org/10.1038/s41524-024-01499-w","url":null,"abstract":"The complex interplay between the rapid martensitic transformation and the plastic relaxation during martensitic transformation in low-carbon steel is investigated using a combined phase-field and phenomenological crystal plasticity approach. The large transformation-induced deformations and local lattice rotations are rigorously described within the finite strain framework. The study reveals that plastic relaxation plays a crucial role in accommodating the transformation-induced deformations of martensite in the parent austenite phase. By systematically varying the plastic slip rate, imposed cooling rate, and carbon content, the simulations provide insights into the interdependence between these factors, contributing to a better understanding of the martensitic transformation process and the resulting microstructures. The phenomenological crystal plasticity model effectively relates the plastic relaxation rate to the rate of martensitic transformation with a significant time scale difference between the two processes. The findings contribute to a deeper understanding of the interplay between the rapid martensitic transformation and the requirement for plastic deformation.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0