npj Computational Materials最新文献

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

Dynamic oxygen-redox evolution of cathode reactions based on the multistate equilibrium potential model 基于多态平衡电位模型的阴极反应动态氧-氧化还原演化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-02 DOI: 10.1038/s41524-025-01714-2

Nian Ran, Chengbo Li, Qinwen Cui, Dezhen Xue, Jianjun Liu

{"title":"Dynamic oxygen-redox evolution of cathode reactions based on the multistate equilibrium potential model","authors":"Nian Ran, Chengbo Li, Qinwen Cui, Dezhen Xue, Jianjun Liu","doi":"10.1038/s41524-025-01714-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01714-2","url":null,"abstract":"Understanding the mechanisms of oxygen anion electrochemical reactions within crystals has long perplexed electrochemical scientists and hindered the structural design and composition optimization of Li-ion cathode materials. Machine learning interatomic potentials (MLIP) are transforming the landscape by enabling high-accuracy atomistic modeling on a large scale in materials science and chemistry. The diversity and comprehensiveness of the dataset are fundamental to building a high-accuracy MLIP. Here, we constructed a Li1.2–xMn0.6Ni0.2O2 (x = 0–1.04) dataset that includes over 15,000 chemical non-equilibrium and chemical equilibrium structures. Using this dataset, we trained an MLIP model (multistate equilibrium potential, named MSEP) with test accuracies of 0.008 eV/atom and 0.153 eV/Å for energy and force, respectively. Through MSEP-MD simulations, we identify a kinetically viable O-redox mechanism in which the formation of transient interlayer O22−, O2− or O3− intermediates drives out-of-plane Mn and Ni migration, resulting in O2 molecules forming within the bulk structure. O3− intermediates have a certain ability to capture O2, which may help alleviate the formation of lattice O2.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547503","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

$$widehat{P}widehat{{T}}$$ symmetry controlled magnetic order switching $$widehat{P}widehat{{T}}$$ 对称控制磁序开关

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-02 DOI: 10.1038/s41524-025-01699-y

Ziyu Niu, Jing Sun, Zekun Zhang, Xiaohong Zheng, Xixiang Jing, Jingjing Wan, Jing Wang, Junqin Shi, Li-min Liu, Weimin Liu, Xiaoli Fan, Tengfei Cao

{"title":"$$widehat{P}widehat{{T}}$$ symmetry controlled magnetic order switching","authors":"Ziyu Niu, Jing Sun, Zekun Zhang, Xiaohong Zheng, Xixiang Jing, Jingjing Wan, Jing Wang, Junqin Shi, Li-min Liu, Weimin Liu, Xiaoli Fan, Tengfei Cao","doi":"10.1038/s41524-025-01699-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01699-y","url":null,"abstract":"Precise electric control of magnetic order and anomalous Hall conductivity (AHC) is pivotal for spintronics. While electric-field control of magnetic order and AHC has been explored in magneto-electric materials, achieving precise and energy-efficient magnetic order switching between two (hat{P}hat{{T}}) symmetry-connected magnetic states remains challenging. Here, we propose the utilization of the combined (widehat{P}widehat{{T}}) symmetry that establishes a direct connection between electric polarization and magnetic orders, to electrically manipulate magnetic order and the AHC. Using 3MnB₂T₄·2B₂T₃ (B = Sb/Bi, T = Se/Te) as an example, we demonstrate that the (widehat{P}widehat{{T}}) connected up-up-down (UUD) and up-down-down (UDD) states exhibit switchable magnetic configurations via electric polarization. The energy difference between the UUD and UDD states is linearly modulated by electric polarizations, enabling full control of the magnetic states via electric field, spontaneous polarization, or even weak sliding ferroelectricity. The findings demonstrate that (widehat{P}widehat{{T}}) symmetry can be well utilized to design electric polarization-controlled magnetic orders and will find important applications in spintronics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"647 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533945","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 multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys 复杂浓缩合金低模量和高屈服强度的多目标协同设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-02 DOI: 10.1038/s41524-025-01713-3

Qingfeng Yin, Yuan Wu, Honghui Wu, Xiaobin Zhang, Suihe Jiang, Hui Wang, Xiongjun Liu, Zhaoping Lu

{"title":"A multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys","authors":"Qingfeng Yin, Yuan Wu, Honghui Wu, Xiaobin Zhang, Suihe Jiang, Hui Wang, Xiongjun Liu, Zhaoping Lu","doi":"10.1038/s41524-025-01713-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01713-3","url":null,"abstract":"Low Young’s modulus and high yield strength are concurrently needed to meet the performance requirements of metallic implant materials. The single-objective performance-oriented alloy design strategies face challenges in effectively addressing the inherent conflict between Young’s modulus and yield strength. In this study, we developed a machine learning model for multi-objective synergistic optimization of modulus and yield strength, successfully enabling simultaneous prediction of Young’s modulus and yield strength in the Ti-Zr-Hf-Nb-Ta-Mo-Sn alloy system. The critical features influencing the modulus and strength of the alloys were systematically analyzed and identified. Moreover, a series of complex concentrated alloy (CCAs) with low Young’s modulus and high yield strength were successfully prepared based on this model. The newly developed alloys exhibited a stable single-phase BCC (body-centered-cubic) structure with Young’s modulus in the range of 40–50 GPa, yield strength of 600–915 MPa, and elastic admissible strain of approximately 1.5%. The multi-objective machine learning model developed in this study can synergistically optimize low Young’s modulus and high yield strength in complex alloys, providing a novel approach for the design of advanced biomedical alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533946","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

Discovery of liquid crystalline polymers with high thermal conductivity using machine learning 利用机器学习发现具有高导热性的液晶聚合物

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-02 DOI: 10.1038/s41524-025-01671-w

Hayato Maeda, Stephen Wu, Rika Marui, Erina Yoshida, Kan Hatakeyama-Sato, Yuta Nabae, Shiori Nakagawa, Meguya Ryu, Ryohei Ishige, Yoh Noguchi, Yoshihiro Hayashi, Masashi Ishii, Isao Kuwajima, Felix Jiang, Xuan Thang Vu, Sven Ingebrandt, Masatoshi Tokita, Junko Morikawa, Ryo Yoshida, Teruaki Hayakawa

{"title":"Discovery of liquid crystalline polymers with high thermal conductivity using machine learning","authors":"Hayato Maeda, Stephen Wu, Rika Marui, Erina Yoshida, Kan Hatakeyama-Sato, Yuta Nabae, Shiori Nakagawa, Meguya Ryu, Ryohei Ishige, Yoh Noguchi, Yoshihiro Hayashi, Masashi Ishii, Isao Kuwajima, Felix Jiang, Xuan Thang Vu, Sven Ingebrandt, Masatoshi Tokita, Junko Morikawa, Ryo Yoshida, Teruaki Hayakawa","doi":"10.1038/s41524-025-01671-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01671-w","url":null,"abstract":"Next-generation power electronics require efficient heat dissipation management, and molecular design guidelines are needed to develop polymers with high thermal conductivity. Polymer materials have considerably lower thermal conductivity than metals and ceramics due to phonon scattering in the amorphous region. The spontaneous orientation of the molecular chains of liquid crystalline polymers could potentially give rise to high thermal conductivity, but the molecular design of such polymers remains largely empirical. In this study, we developed a machine learning model that predicts with more than 96% accuracy whether liquid crystalline states will form based on the chemical structure of the polymer. By exploring the inverse mapping of this model, we identified a comprehensive set of chemical structures for liquid crystalline polyimides. The polymers were then experimentally synthesized, and the results confirmed that they form liquid crystalline phases, with all polymers exhibiting calculated thermal conductivities within the range of 0.722–1.26 W m−1 K−1.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533947","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

Virtual experiments in computational magnetism with mag2exp mag2exp在计算磁学中的虚拟实验

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01686-3

Samuel J. R. Holt, Martin Lang, James C. Loudon, Thomas J. Hicken, Dieter Suess, David Cortés-Ortuño, Swapneel A. Pathak, Marijan Beg, Kauser Zulfiqar, Hans Fangohr

引用次数: 0

Machine learning and data-driven methods in computational surface and interface science 计算表面和界面科学中的机器学习和数据驱动方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01691-6

Lukas Hörmann, Wojciech G. Stark, Reinhard J. Maurer

引用次数: 0

Optimization of Coulomb energies in gigantic configurational spaces of multi-element ionic crystals 多元素离子晶体巨大构型空间中库仑能的优化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01690-7

Konstantin Köster, Tobias Binninger, Payam Kaghazchi

引用次数: 0