npj Computational Materials最新文献_第8页

Photo-induced manipulation and relaxation dynamics of Weyl-semimetals weyl -半金属的光诱导操纵和弛豫动力学

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-07 DOI: 10.1038/s41524-025-01708-0

Jakub Šebesta, Oscar Grånäs

引用次数: 0

Hamiltonian transformation for accurate and efficient band structure interpolation 哈密顿变换用于精确有效的波段结构插值

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-07 DOI: 10.1038/s41524-025-01684-5

Kai Wu, Yingzhou Li, Wentiao Wu, Lin Lin, Wei Hu, Jinlong Yang

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Enhancing transferability of machine learning-based polarizability models in condensed-phase systems via atomic polarizability constraint 通过原子极化率约束增强凝聚相系统中基于机器学习的极化率模型的可转移性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-05 DOI: 10.1038/s41524-025-01705-3

Mandi Fang, Yinqiao Zhang, Zheyong Fan, Daquan Tan, Xiaoyong Cao, Chunlei Wei, Nan Xu, Yi He

{"title":"Enhancing transferability of machine learning-based polarizability models in condensed-phase systems via atomic polarizability constraint","authors":"Mandi Fang, Yinqiao Zhang, Zheyong Fan, Daquan Tan, Xiaoyong Cao, Chunlei Wei, Nan Xu, Yi He","doi":"10.1038/s41524-025-01705-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01705-3","url":null,"abstract":"Accurate prediction of molecular polarizability is essential for understanding electrical, optical, and dielectric properties of materials. Traditional quantum mechanical (QM) methods, though precise for small systems, are computationally prohibitive for large-scale systems. In this work, we proposed an efficient approach for calculating molecular polarizability of condensed-phase systems by embedding atomic polarizability constraints into the tensorial neuroevolution potential (TNEP) framework. Using n-heneicosane as a benchmark, a training data set was constructed from molecular clusters truncated from the bulk systems. Atomic polarizabilities derived from semi-empirical QM calculations were integrated as training constraints for its balance of computational efficiency and physical interpretability. The constrained TNEP model demonstrated improved accuracy in predicting molecular polarizabilities for larger clusters and condensed-phase systems, attributed to the model’s refined ability to properly partition molecular polarizabilities into atomic contributions across systems with diverse configurational features. Results highlight the potential of the TNEP model with atomic polarizability constraint as a generalizable strategy to enhance the scalability and transferability of other atom-centered machine learning-based polarizability models, offering a promising solution for simulating large-scale systems with high data efficiency.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566156","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

High-throughput discovery of perturbation-induced topological magnons 微扰诱导拓扑磁振子的高通量发现

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-05 DOI: 10.1038/s41524-025-01706-2

Mohammed J. Karaki, Ahmed E. Fahmy, Archibald J. Williams, Sara Haravifard, Joshua E. Goldberger, Yuan-Ming Lu

{"title":"High-throughput discovery of perturbation-induced topological magnons","authors":"Mohammed J. Karaki, Ahmed E. Fahmy, Archibald J. Williams, Sara Haravifard, Joshua E. Goldberger, Yuan-Ming Lu","doi":"10.1038/s41524-025-01706-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01706-2","url":null,"abstract":"Topological magnons give rise to possibilities for engineering novel spintronics devices with critical applications in quantum information and computation, due to their symmetry-protected robustness and low dissipation. However, to make reliable and systematic predictions about the material realization of topological magnons has been a major challenge, due to the lack of neutron scattering data for most materials and the absence of reliable ab initio calculations for magnons. In this work, we significantly advance the symmetry-based approach for identifying topological magnons through developing a fully automated algorithm, utilizing the theory of symmetry indicators, that enables a highly efficient and large-scale search for candidate materials hosting perturbation-driven topological magnons. This progress not only streamlines the discovery process but also expands the scope of materials exploration beyond previous manual or traditional approaches, offering a powerful tool for uncovering novel topological phases in magnetic systems. Performing a large-scale search over all 1649 magnetic materials in the Bilbao Crystallographic Server (BCS) with a commensurate magnetic order, we discover 387 perturbation-induced topological magnon materials, significantly expanding the pool of topological magnon materials and showing that more than 23% of all commensurate magnetic compounds in the BCS database are topological. We further discuss examples and experimental accessibility of the candidate materials, shedding light on future experimental realizations of topological magnons in magnetic materials. We provide an open-source program that checks the symmetry-enforced magnon band topology of any commensurate magnetic structure upon perturbations and allows researchers to reproduce our results.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"40 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566155","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-driven molecular dynamics decodes thermal tuning in graphene foam composites 机器学习驱动的分子动力学解码石墨烯泡沫复合材料的热调谐

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-05 DOI: 10.1038/s41524-025-01710-6

Pingyang Zhang, Shaodong Zhang, Yihan Qin, Tingting Du, Lei Wei, Xiangyu Li

{"title":"Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites","authors":"Pingyang Zhang, Shaodong Zhang, Yihan Qin, Tingting Du, Lei Wei, Xiangyu Li","doi":"10.1038/s41524-025-01710-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01710-6","url":null,"abstract":"Graphene foam (GF), synthesized via Chemical Vapor Deposition (CVD), has been proven to be the ideal bulk porous material. The addition of poly(dimethylsiloxane) (PDMS) within the porous structure enables enhancement of mechanical strength and alteration of heat transfer behavior. This study focuses on the thermodynamic behavior of GF/PDMS composites during deformation, and employs stochastic modeling and neuroevolution potential (NEP) for complex material modeling with precise prediction of microscopic mechanisms governing thermal property variations. The results demonstrate that the composite with a 5% doping rate of PDMS achieves the optimal mechanical performance and shows a 7.13-fold modulation in thermal resistance during the deformation from 40% stretching to 50% compression. Findings indicate PDMS fortifies structural stability while enabling dynamic thermal conductivity modulation in GF. This research provides critical insights into the micro-mechanisms of GF/PDMS composites and offers a theoretical foundation for applications in dynamic thermal management and self-powered sensor networks.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566158","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

Teaching oxidation states to neural networks 向神经网络教授氧化态

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-04 DOI: 10.1038/s41524-025-01709-z

Cristiano Malica, Nicola Marzari

引用次数: 0

Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion 机器学习加速了CO2到甲醇转化中催化剂发现的描述符设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-04 DOI: 10.1038/s41524-025-01664-9

Prajwal Pisal, Ondřej Krejčí, Patrick Rinke

引用次数: 0

Data-driven microstructural optimization of Ag-Bi-I perovskite-inspired materials Ag-Bi-I钙钛矿激发材料的数据驱动微结构优化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-03 DOI: 10.1038/s41524-025-01701-7

Kshithij Mysore Nandishwara, Shuan Cheng, Pengjun Liu, Huimin Zhu, Xiaoyu Guo, Fabien C.-P. Massabuau, Robert L. Z. Hoye, Shijing Sun

{"title":"Data-driven microstructural optimization of Ag-Bi-I perovskite-inspired materials","authors":"Kshithij Mysore Nandishwara, Shuan Cheng, Pengjun Liu, Huimin Zhu, Xiaoyu Guo, Fabien C.-P. Massabuau, Robert L. Z. Hoye, Shijing Sun","doi":"10.1038/s41524-025-01701-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01701-7","url":null,"abstract":"Microstructural design is crucial yet challenging for thin-film semiconductors, creating barriers for new materials to achieve practical applications in photovoltaics and optoelectronics. We present the Daisy Visual Intelligence Framework (Daisy), which combines multiple AI models to learn from historical microscopic images and propose new synthesis conditions towards desirable microstructures. Daisy consists of an image interpreter to extract grain and defect statistics, and a reinforcement-learning-driven synthesis planner to optimize thin-film morphology. Using Ag-Bi-I perovskite-inspired materials as a case study, Daisy achieved over 120× and 87× acceleration in image analysis and synthesis planning, respectively, compared to manual methods. Processing parameters for AgBiI4 were optimized from over 1700 possible synthesis conditions within 3.5 min, yielding experimentally validated films with no visible pinholes and average grain sizes 14.5% larger than the historical mean. Our work advances computational frameworks for self-driving labs and shedding light on AI-accelerated microstructure development for emerging thin-film materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"647 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547464","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

Simulation of intercalation and phase transitions in nano-porous, polycrystalline agglomerates 纳米多孔多晶团聚体的插层和相变模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-03 DOI: 10.1038/s41524-025-01707-1

Simon Daubner, Marcel Weichel, Martin Reder, Daniel Schneider, Qi Huang, Alexander E. Cohen, Martin Z. Bazant, Britta Nestler

{"title":"Simulation of intercalation and phase transitions in nano-porous, polycrystalline agglomerates","authors":"Simon Daubner, Marcel Weichel, Martin Reder, Daniel Schneider, Qi Huang, Alexander E. Cohen, Martin Z. Bazant, Britta Nestler","doi":"10.1038/s41524-025-01707-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01707-1","url":null,"abstract":"Optimal microstructure design of battery materials is critical to enhance the performance of batteries for tailored applications such as high power cells. Accurate simulation of the thermodynamics, transport, and electrochemical reaction kinetics in commonly used polycrystalline battery materials remains a challenge. Here, we combine state-of-the-art multiphase field modelling with the smoothed boundary method to accurately simulate complex battery microstructures and multiphase physics. The phase-field method is employed to parameterize complex open pore cathode microstructures and we present a formulation to impose galvanostatic charging conditions on the diffuse boundary representation. By extending the smoothed boundary method to the multiphase-field method, we build a simulation framework which is capable of simulating the coupled effects of intercalation, anisotropic diffusion, and phase transitions in arbitrary complex polycrystalline agglomerates. This method is directly compatible with voxel-based data, e.g., from X-ray tomography. The simulation framework is used to study the reversible phase transitions in LiXNiO2 in dense and nanoporous agglomerates. Based on the thermodynamic consistency of phase-field approaches with ab-initio simulations and the open circuit potential, we reconstruct the Gibbs free energies of four individual phases (H1, M, H2 and H3) from experimental cycling data. The results show remarkable agreement with previously published DFT results. From charge simulations, we discover a strong influence of particle morphology on the phase transition behaviour, in particular a shrinking core-like behaviour in dense polycrystalline structures and a particle-by-particle mosaic behavior in nanoporous samples. Overall, the proposed simulation framework enables the detailed study of phase transitions in intercalation materials to enhance microstructure design and fast charging protocols.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"35 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547502","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

Materials design with target-oriented Bayesian optimization 面向目标的贝叶斯优化材料设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-03 DOI: 10.1038/s41524-025-01704-4

Yuan Tian, Tongtong Li, Jianbo Pang, Yumei Zhou, Dezhen Xue, Xiangdong Ding, Turab Lookman

{"title":"Materials design with target-oriented Bayesian optimization","authors":"Yuan Tian, Tongtong Li, Jianbo Pang, Yumei Zhou, Dezhen Xue, Xiangdong Ding, Turab Lookman","doi":"10.1038/s41524-025-01704-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01704-4","url":null,"abstract":"Materials design using Bayesian optimization (BO) typically focuses on optimizing materials properties by estimating the maxima/minima of unknown functions. However, materials often possess good properties at specific values or show effective response under certain conditions. We propose a target-oriented BO to efficiently suggest materials with target-specific properties. The method samples potential candidates by allowing their properties to approach the target value from either above or below, minimizing experimental iterations. We compare the performance of target-oriented BO with that of other BO methods on synthetic functions and materials databases. The average results from hundreds of repeated trials demonstrate target-oriented BO requires fewer experimental iterations to reach the same target, especially when the training dataset is small. We further employ the method to discover a thermally-responsive shape memory alloy Ti0.20Ni0.36Cu0.12Hf0.24Zr0.08 with a transformation temperature difference of only 2.66 °C (0.58% of the range) from the target temperature in 3 experimental iterations. Our method provides a solution tailored for optimizing target-specific properties, facilitating the accelerated development of materials with predefined properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"76 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547501","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