npj Computational Materials最新文献_第6页

Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network 基于等变注意张量图神经网络的压电张量精确预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-06 DOI: 10.1038/s41524-025-01546-0

Luqi Dong, Xuanlin Zhang, Ziduo Yang, Lei Shen, Yunhao Lu

{"title":"Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network","authors":"Luqi Dong, Xuanlin Zhang, Ziduo Yang, Lei Shen, Yunhao Lu","doi":"10.1038/s41524-025-01546-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01546-0","url":null,"abstract":"The piezoelectric materials enable the mutual conversion between mechanical and electrical energy, which drive a multi-billion dollar industry through their applications as sensors, actuators, and energy harvesters. The third-rank piezoelectric tensor is the core matrices for piezoelectric materials and their devices. However, the high costs of obtaining full piezoelectric tensor data through either experimental or computational methods make a significant challenge. Here, we propose an equivariant attention tensor graph neural network (EATGNN) that can identify crystal symmetry and remain independent of the reference frame, ultimately enabling the accurate prediction of the complete third-rank piezoelectric tensor. Especially, we perform an irreducible decomposition of the piezoelectric tensor into four irreducible representations to efficiently reserve the symmetry under group transformation operations. Our results further demonstrate that this model performs well in both bulk and two-dimensional materials. Finally, combining EATGNN with first-principles calculations, we discovered several potential high-performance piezoelectric materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561222","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

A machine-learning framework for accelerating spin-lattice relaxation simulations 加速自旋晶格弛豫模拟的机器学习框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-06 DOI: 10.1038/s41524-025-01547-z

Valerio Briganti, Alessandro Lunghi

引用次数: 0

Constructing multicomponent cluster expansions with machine-learning and chemical embedding 用机器学习和化学嵌入构造多组分聚类展开

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-06 DOI: 10.1038/s41524-025-01543-3

Yann L. Müller, Anirudh Raju Natarajan

{"title":"Constructing multicomponent cluster expansions with machine-learning and chemical embedding","authors":"Yann L. Müller, Anirudh Raju Natarajan","doi":"10.1038/s41524-025-01543-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01543-3","url":null,"abstract":"Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the embedded cluster expansion (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"67 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561219","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

Foundation models for materials discovery – current state and future directions 材料发现的基础模型——现状和未来方向

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-06 DOI: 10.1038/s41524-025-01538-0

Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, Alessandro Curioni

引用次数: 0

Virtual melting and cyclic transformations between amorphous Si, Si I, and Si IV in a shear band at room temperature 非晶态Si、Si I和Si IV在室温剪切带中的虚熔化和循环转变

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-03 DOI: 10.1038/s41524-025-01537-1

Hao Chen, Valery I. Levitas

{"title":"Virtual melting and cyclic transformations between amorphous Si, Si I, and Si IV in a shear band at room temperature","authors":"Hao Chen, Valery I. Levitas","doi":"10.1038/s41524-025-01537-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01537-1","url":null,"abstract":"Virtual melting (VM) as alternative deformation and stress relaxation mechanisms under extreme load is directly validated by molecular dynamics (MD) simulations of the simple shear of single crystal Si I at a temperature 1383 K below the melting temperature. The shear band consisting of liquid Si is formed immediately after the shear instability while stresses drop to zero. This process is independent of the applied shear rate. A new thermodynamic approach is developed, and the thermodynamic criterion for VM, which depends on the ratio of the sample to shear band widths, is derived analytically and confirmed by MD simulations. Since stress-free melt is unstable at 300 K, with further shear, the VM immediately transforms to a mixture of low-density amorphous a-Si, stable Si I, and metastable Si IV. Cyclic transformations between a-Si ↔ Si I, a-Si ↔ Si IV, and Si I ↔ Si IV with volume fraction of all phases mostly between 0.2 and 0.4 and non-repeatable nanostructure evolution are reveled. Such cyclic transformations produce additional important carriers for plastic deformation through transformation strain and transformation-induced plasticity due to volume change, which may occur in shear bands in various material systems but missed in experiments and simulations. The release of shear stresses quenches the microstructure, and shows reasonable qualitative correspondence with existing experiments.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539233","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

MGNN: Moment Graph Neural Network for Universal Molecular Potentials MGNN：通用分子势矩图神经网络

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-025-01541-5

Jian Chang, Shuze Zhu

{"title":"MGNN: Moment Graph Neural Network for Universal Molecular Potentials","authors":"Jian Chang, Shuze Zhu","doi":"10.1038/s41524-025-01541-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01541-5","url":null,"abstract":"The quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neural networks has marked a transformative era in graph-based learning, particularly in the realm of predicting chemical properties and expediting molecular dynamics studies. We present the Moment Graph Neural Network (MGNN), a rotation-invariant message passing neural network architecture that capitalizes on the moment representation learning of 3D molecular graphs, is adept at capturing the nuanced spatial relationships inherent in three-dimensional molecular structures. From benchmark tests on public datasets, MGNN delivers multiple state-of-the-art results on QM9, revised MD17 and MD17-ethanol. Its generalizability and efficiency are also tested in additional systems including 3BPA and 25-element high-entropy alloys. The prowess of MGNN also extends to dynamic simulations, accurately predicting the structural and kinetic properties of complex systems such as amorphous electrolytes, with results that closely align with those from ab-initio simulations. The application of MGNN to the simulation of molecular spectra exemplifies its potential to offer a promising alternative to traditional electronic structure methods.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"55 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528275","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

Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph embeddings 利用多步非线性自回归和图嵌入进行快速高保真量子模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-024-01479-0

Akeel A. Shah, P. K. Leung, W. W. Xing

{"title":"Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph embeddings","authors":"Akeel A. Shah, P. K. Leung, W. W. Xing","doi":"10.1038/s41524-024-01479-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01479-0","url":null,"abstract":"The design and high-throughput screening of materials using machine-learning assisted quantum-mechanical simulations typically requires the existence of a very large data set, often generated from simulations at a high level of theory or fidelity. A single simulation at high fidelity can take on the order of days for a complex molecule. Thus, although machine learning surrogate simulations seem promising at first glance, generation of the training data can defeat the original purpose. For this reason, the use of machine learning to screen or design materials remains elusive for many important applications. In this paper we introduce a new multi-fidelity approach based on a dual graph embedding to extract features that are placed inside a nonlinear multi-step autoregressive model. Experiments on five benchmark problems, with 14 different quantities and 27 different levels of theory, demonstrate the generalizability and high accuracy of the approach. It typically requires a few 10s to a few 1000’s of high-fidelity training points, which is several orders of magnitude lower than direct ML methods, and can be up to two orders of magnitude lower than other multi-fidelity methods. Furthermore, we develop a new benchmark data set for 860 benzoquinone molecules with up to 14 atoms, containing energy, HOMO, LUMO and dipole moment values at four levels of theory, up to coupled cluster with singles and doubles.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528247","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

Design of 2D skyrmionic metamaterials through controlled assembly 通过受控装配设计二维天磁超材料

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-025-01534-4

Qichen Xu, Zhuanglin Shen, Alexander Edström, I. P. Miranda, Zhiwei Lu, Anders Bergman, Danny Thonig, Wanjian Yin, Olle Eriksson, Anna Delin

{"title":"Design of 2D skyrmionic metamaterials through controlled assembly","authors":"Qichen Xu, Zhuanglin Shen, Alexander Edström, I. P. Miranda, Zhiwei Lu, Anders Bergman, Danny Thonig, Wanjian Yin, Olle Eriksson, Anna Delin","doi":"10.1038/s41524-025-01534-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01534-4","url":null,"abstract":"Despite extensive research on magnetic skyrmions and antiskyrmions, a significant challenge remains in crafting nontrivial high-order skyrmionic textures with varying, or even tailor-made, topologies. We address this challenge, by focusing on a construction pathway of skyrmionic metamaterials within a monolayer thin film and suggest several skyrmionic metamaterials that are surprisingly stable, i.e., long-lived, due to a self-stabilization mechanism. This makes these new textures promising for applications. Central to our approach is the concept of ’simulated controlled assembly’, in short, a protocol inspired by ’click chemistry’ that allows for positioning topological magnetic structures where one likes, and then allowing for energy minimization to elucidate the stability. Utilizing high-throughput atomistic-spin-dynamic simulations alongside state-of-the-art AI-driven tools, we have isolated skyrmions (topological charge Q = 1), antiskyrmions (Q = − 1), and skyrmionium (Q = 0). These entities serve as foundational ’skyrmionic building blocks’ to form the here-reported intricate textures. In this work, two key contributions are introduced to the field of skyrmionic systems. First, we present a novel combination of atomistic spin dynamics simulations and controlled assembly protocols for the stabilization and investigation of new topological magnets. Second, using the aforementioned methods we report on the discovery of skyrmionic metamaterials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"189 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528262","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

Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations 通过机器学习驱动的原子模拟了解动态压缩条件下的α-石英相变

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-02 DOI: 10.1038/s41524-025-01542-4

Linus C. Erhard, Christoph Otzen, Jochen Rohrer, Clemens Prescher, Karsten Albe

引用次数: 0

Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI 通过高通量分子动力学和可解释的人工智能探索高性能粘度指数改善聚合物

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-01 DOI: 10.1038/s41524-025-01539-z

Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou

{"title":"Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI","authors":"Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou","doi":"10.1038/s41524-025-01539-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01539-z","url":null,"abstract":"Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline, we explored high-performance viscosity index improver polymers and constructed a dataset of 1166 entries for viscosity index improvers (VII) started from only five types of polymers. Under multi-objective constraints, 366 potential high-viscosity-temperature performance polymers were identified, and six representative polymers were validated through direct MD simulations. Starting from high-dimensional physical features, we conducted an unbiased systematic analysis of the quantitative structure-property relationships for polymers VII, providing an explicit mathematical model with promising application in VII industry. This work demonstrates the advanced capabilities and reliability of the pipeline proposed here in initiating material innovation cycles in data-scarce fields, and the establishment of the VII dataset and models will serve as a critical starting point for the data-driven design of high viscosity-temperature performance polymers.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"34 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526061","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