npj Computational Materials最新文献_第8页

CELL: a Python package for cluster expansion with a focus on complex alloys CELL：用于集群扩展的 Python 软件包，侧重于复杂合金

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-30 DOI: 10.1038/s41524-024-01363-x

Santiago Rigamonti, Maria Troppenz, Martin Kuban, Axel Hübner, Claudia Draxl

{"title":"CELL: a Python package for cluster expansion with a focus on complex alloys","authors":"Santiago Rigamonti, Maria Troppenz, Martin Kuban, Axel Hübner, Claudia Draxl","doi":"10.1038/s41524-024-01363-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01363-x","url":null,"abstract":"We present the Python package CELL, which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-, two-, and three-dimensional alloys, in a general multi-component and multi-sublattice framework. It is capable of dealing with complex materials comprising several atoms in their parent lattice. CELL uses state-of-the-art techniques for the construction of training data sets, model selection, and finite-temperature simulations. The user interface consists of well-documented Python classes and modules (http://sol.physik.hu-berlin.de/cell/). CELL also provides visualization utilities and can be interfaced with virtually any ab initio package, total-energy codes based on interatomic potentials, and more. The usage and capabilities of CELL are illustrated by a number of examples, comprising a Cu-Pt surface alloy with oxygen adsorption, featuring two coupled binary sublattices, and the thermodynamic analysis of its order-disorder transition; the demixing transition and lattice-constant bowing of the Si-Ge alloy; and an iterative CE approach for a complex clathrate compound with a parent lattice consisting of 54 atoms.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"6 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101635","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

Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching 铁电尖端诱导电开关三维相场建模的机器学习代用工具

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-30 DOI: 10.1038/s41524-024-01375-7

Kévin Alhada–Lahbabi, Damien Deleruyelle, Brice Gautier

引用次数: 0

Distinct amorphization resistance in high-entropy MAX-phases (Ti, M)2AlC (M=Nb, Ta, V, Zr) under in situ irradiation 原位辐照条件下高熵 MAX 相 (Ti, M)2AlC (M=Nb, Ta, V, Zr) 的抗非晶化性差异

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-30 DOI: 10.1038/s41524-024-01370-y

Hao Xiao, Shuang Zhao, Jun Zhang, Shijun Zhao, Youbing Li, Ke Chen, Liuxuan Cao, Yugang Wang, Qing Huang, Chenxu Wang

{"title":"Distinct amorphization resistance in high-entropy MAX-phases (Ti, M)2AlC (M=Nb, Ta, V, Zr) under in situ irradiation","authors":"Hao Xiao, Shuang Zhao, Jun Zhang, Shijun Zhao, Youbing Li, Ke Chen, Liuxuan Cao, Yugang Wang, Qing Huang, Chenxu Wang","doi":"10.1038/s41524-024-01370-y","DOIUrl":"https://doi.org/10.1038/s41524-024-01370-y","url":null,"abstract":"High-entropy materials have been proposed for applications in nuclear systems recently due to their outstanding properties in extreme environments. Chemical complexity in these materials plays an important role in irradiation tolerance since it significantly affects energy dissipation and defect behaviors under particle bombardment. Indeed, better resistance to irradiation-induced amorphization was observed in the high-entropy MAX (HE-MAX) phase (Ti, M)2SnC (M = V, Nb, Zr, Hf). However, in this work, we report an opposite trend in another series of HE-MAX phases (Ti, M)2AlC (M = Nb, Ta, V, Zr). It is demonstrated that the amorphization resistance is sequentially reduced as the number of components increases from single-component Ti2AlC to (TiNbTa)2AlC and (TiNbTaVZr)2AlC. These phenomena are verified through AIMD simulations and interpreted by analyzing the underlying properties combining lattice distortion and bonding characteristics through the first-principle calculation. By developing a machine-learning (ML) model, we can directly predict lattice distortion to screen HE-MAX phases with excellent resistance to irradiation-induced amorphization. We highlight that the elemental species plays a more crucial role in the irradiation tolerance of these MAX phases than the number of constituent elements. Knowledge gained from this study will enable an improved understanding of the irradiation tolerance in HE-MAX phases and other multi-elemental ceramics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101632","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 deep generative modeling architecture for designing lattice-constrained perovskite materials 用于设计晶格受限包晶材料的深度生成建模架构

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-30 DOI: 10.1038/s41524-024-01381-9

Ericsson Tetteh Chenebuah, Michel Nganbe, Alain Beaudelaire Tchagang

{"title":"A deep generative modeling architecture for designing lattice-constrained perovskite materials","authors":"Ericsson Tetteh Chenebuah, Michel Nganbe, Alain Beaudelaire Tchagang","doi":"10.1038/s41524-024-01381-9","DOIUrl":"https://doi.org/10.1038/s41524-024-01381-9","url":null,"abstract":"In modern materials discovery, materials are now efficiently screened using machine learning (ML) techniques with target-specific properties for meeting various engineering applications. However, a major challenge that persists with deep generative ML approach is the issue related to lattice reconstruction at the decoding phase, leading to the generation of materials with low symmetry, unfeasible atomic coordination, and triclinic behavioral properties in the crystal lattice. To address this concern, the present research makes a contribution by proposing a Lattice-Constrained Materials Generative Model (LCMGM) for designing new and polymorphic perovskite materials with crystal conformities that are consistent with predefined geometrical and thermodynamic stability constraints at the encoding phase. A comparison with baseline models such as Physics Guided Crystal Generative Model (PGCGM) and Fourier-Transformed Crystal Property (FTCP), confirms the potential of the LCMGM for improved training stability, better chemical learning effect and higher geometrical conformity. The new materials emerging from this research are Density Functional Theory (DFT) validated and openly made available in the Mendeley data repository: https://doi.org/10.17632/m262xxpgn2.1.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101631","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

Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence 通过高通量实验和人工智能开发新的氧进化反应电催化剂

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-28 DOI: 10.1038/s41524-024-01386-4

Shaomeng Xu, Zhuyang Chen, Mingyang Qin, Bijun Cai, Weixuan Li, Ronggui Zhu, Chen Xu, X.-D. Xiang

{"title":"Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence","authors":"Shaomeng Xu, Zhuyang Chen, Mingyang Qin, Bijun Cai, Weixuan Li, Ronggui Zhu, Chen Xu, X.-D. Xiang","doi":"10.1038/s41524-024-01386-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01386-4","url":null,"abstract":"The development of non-noble metal electrocatalysts for the Oxygen Evolution Reaction (OER) is advancing towards the use of multi-element materials. To reveal the complex correlations of multi-element OER electrocatalysts, we developed an iterative workflow combining high-throughput experiments and AI-generated content (AIGC) processes. An increased number of 909 (compared to 145 in previous literature) universal descriptors for inorganic materials science were constructed and used as Artificial Neural Network (ANN) input. A large number of statistical ensembles with each ANN individual ensemble having a reduced number of descriptors were integrated with a new Hierarchical Neural Network (HNN) algorithm. This algorithm addresses the longstanding challenge of balancing overwhelming descriptor numbers with insufficient datasets in traditional ANN approaches to materials science problems. As a result, the combination of AIGC and experimental validation significantly enhanced prediction accuracy, increase the R2 values from 0.7 to 0.98 for Tafel slopes.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"37 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090343","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

Stretched non-negative matrix factorization 拉伸非负矩阵因式分解

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-27 DOI: 10.1038/s41524-024-01377-5

Ran Gu, Yevgeny Rakita, Ling Lan, Zach Thatcher, Gabrielle E. Kamm, Daniel O’Nolan, Brennan Mcbride, Allison Wustrow, James R. Neilson, Karena W. Chapman, Qiang Du, Simon J. L. Billinge

{"title":"Stretched non-negative matrix factorization","authors":"Ran Gu, Yevgeny Rakita, Ling Lan, Zach Thatcher, Gabrielle E. Kamm, Daniel O’Nolan, Brennan Mcbride, Allison Wustrow, James R. Neilson, Karena W. Chapman, Qiang Du, Simon J. L. Billinge","doi":"10.1038/s41524-024-01377-5","DOIUrl":"https://doi.org/10.1038/s41524-024-01377-5","url":null,"abstract":"A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085672","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

Learning dislocation dynamics mobility laws from large-scale MD simulations 从大规模 MD 模拟中学习位错动力学流动规律

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-27 DOI: 10.1038/s41524-024-01378-4

Nicolas Bertin, Vasily V. Bulatov, Fei Zhou

{"title":"Learning dislocation dynamics mobility laws from large-scale MD simulations","authors":"Nicolas Bertin, Vasily V. Bulatov, Fei Zhou","doi":"10.1038/s41524-024-01378-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01378-4","url":null,"abstract":"By dispensing with all the atoms and only focusing on dislocation lines, the computational method of Discrete Dislocation Dynamics (DDD) gains greatly over Molecular Dynamics (MD) in simulation efficiency of metal plasticity. But whereas in MD dislocations follow natural dynamics of atomic motion, DDD must rely on a dislocation mobility function to prescribe how a dislocation line should respond to the driving force exerted on it. However, reflecting our still incomplete understanding of ways in which dislocations move, mobility functions presently employed in DDD simulations entail simplifications and approximations of limited or, worse still, unknown accuracy and applicability. Here we introduce a data-driven approach in which the dislocation mobility function is modeled as a graph neural network (GNN) trained on large-scale MD simulations of crystal plasticity. We apply our proposed approach to predicting plastic strength of body-centered-cubic (BCC) metal tungsten and show that, once implemented in a DDD model, our GNN dislocation mobility function accurately reproduces the challenging tension/compression asymmetry of plastic flow observed both in ground-truth MD simulations and in experiment. Furthermore, subsequently validated by MD simulations, the same function accurately predicts plastic response of tungsten under conditions not previously seen in training. By demonstrating its ability to learn relevant physics of dislocation motion, our DDD+ML approach opens a promising avenue to bringing fidelity of DDD models closer in line with direct MD simulations at a much reduced computational cost.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"98 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085671","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

Dynamical phase-field model of cavity electromagnonic systems 空腔电磁系统的动态相场模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-26 DOI: 10.1038/s41524-024-01380-w

Shihao Zhuang, Yujie Zhu, Changchun Zhong, Liang Jiang, Xufeng Zhang, Jia-Mian Hu

{"title":"Dynamical phase-field model of cavity electromagnonic systems","authors":"Shihao Zhuang, Yujie Zhu, Changchun Zhong, Liang Jiang, Xufeng Zhang, Jia-Mian Hu","doi":"10.1038/s41524-024-01380-w","DOIUrl":"https://doi.org/10.1038/s41524-024-01380-w","url":null,"abstract":"Cavity electromagnonic system, which simultaneously consists of cavities for photons, magnons (quanta of spin waves), and acoustic phonons, provides an exciting platform to achieve coherent energy transduction among different physical systems down to single quantum level. Here we report a dynamical phase-field model that allows simulating the coupled dynamics of the electromagnetic waves, magnetization, and strain in 3D multiphase systems. As examples of application, we computationally demonstrate the excitation of hybrid magnon-photon modes (magnon polaritons), Floquet-induced magnonic Aulter-Townes splitting, dynamical energy exchange (Rabi oscillation) and relative phase control (Ramsey interference) between the two magnon polariton modes. The simulation results are consistent with analytical calculations based on Floquet Hamiltonian theory. Simulations are also performed to design a cavity electro-magno-mechanical system that enables the triple phonon-magnon-photon resonance, where the resonant excitation of a chiral, fundamental (n = 1) transverse acoustic phonon mode by magnon polaritons is demonstrated. With the capability to predict coupling strength, dissipation rates, and temporal evolution of photon/magnon/phonon mode profiles using fundamental materials parameters as the inputs, the present dynamical phase-field model represents a valuable computational tool to guide the fabrication of the cavity electromagnonic system and the design of operating conditions for applications in quantum sensing, transduction, and communication.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085089","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 interlayer twist effectively regulates interlayer excitons in InSe/Sb van der Waals heterostructure 层间扭曲有效调节 InSe/Sb 范德华异质结构中的层间激子

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-26 DOI: 10.1038/s41524-024-01384-6

Anqi Shi, Ruilin Guan, Jin Lv, Zifan Niu, Wenxia Zhang, Shiyan Wang, Xiuyun Zhang, Bing Wang, Xianghong Niu

引用次数: 0

High-throughput screening to identify two-dimensional layered phase-change chalcogenides for embedded memory applications 通过高通量筛选确定用于嵌入式存储器应用的二维层状相变卤化合金

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-25 DOI: 10.1038/s41524-024-01387-3

Suyang Sun, Xiaozhe Wang, Yihui Jiang, Yibo Lei, Siyu Zhang, Sanjay Kumar, Junying Zhang, En Ma, Riccardo Mazzarello, Jiang-Jing Wang, Wei Zhang

{"title":"High-throughput screening to identify two-dimensional layered phase-change chalcogenides for embedded memory applications","authors":"Suyang Sun, Xiaozhe Wang, Yihui Jiang, Yibo Lei, Siyu Zhang, Sanjay Kumar, Junying Zhang, En Ma, Riccardo Mazzarello, Jiang-Jing Wang, Wei Zhang","doi":"10.1038/s41524-024-01387-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01387-3","url":null,"abstract":"Chalcogenide phase-change materials (PCMs) are showing versatile possibilities in cutting-edge applications, including non-volatile memory, neuromorphic computing, and nano-photonics. However, for embedded phase-change memory applications, conventional PCMs suffer from insufficient thermal stability because of their relatively low crystallization temperatures (Tx). Although doping with additional alloying elements could improve the amorphous stability, it also increases the tendency towards compositional partitioning and phase separation. Recently, a two-dimensional (2D) layered compound CrGeTe3 (CrGT) was developed as a PCM, showing a high Tx ~ 276 °C with an inverse change in resistive-switching character upon phase transition. Here, we report a high-throughput materials screening for 2D layered phase-change chalcogenides. We aim to clarify whether the high Tx and the inverse electrical resistance contrast are intrinsic features of 2D PCMs. In total, twenty-five 2D chalcogenides with CrGT trilayer structures have been identified from a large database. We then focused on selected layered tellurides by performing thorough ab initio simulations and experimental investigations and confirming that their amorphous phase indeed has a much higher Tx than conventional PCMs. We attribute this enhanced amorphous stability to the structurally complex nuclei required to render crystallization possible. Overall, we regard InGeTe3 as a balanced 2D PCM with both high thermal stability and large electrical contrast for embedded memory applications.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050642","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