npj Computational Materials最新文献_第9页

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

Data-driven low-rank approximation for the electron-hole kernel and acceleration of time-dependent GW calculations 数据驱动的电子空穴核的低秩近似和时变GW计算的加速

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01680-9

Bowen Hou, Jinyuan Wu, Victor Chang Lee, Jiaxuan Guo, Luna Y. Liu, Diana Y. Qiu

{"title":"Data-driven low-rank approximation for the electron-hole kernel and acceleration of time-dependent GW calculations","authors":"Bowen Hou, Jinyuan Wu, Victor Chang Lee, Jiaxuan Guo, Luna Y. Liu, Diana Y. Qiu","doi":"10.1038/s41524-025-01680-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01680-9","url":null,"abstract":"Many-body interactions are essential for understanding non-linear optics and ultrafast spectroscopy of materials. Recent first principles approaches based on nonequilibrium Green’s function formalisms, such as the time-dependent adiabatic GW (TD-aGW) approach, can predict nonequilibrium dynamics of excited states including electron-hole interactions. However, the high-dimensionality of the electron-hole kernel poses significant computational challenges. Here, we develop a data-driven low-rank approximation for the electron-hole kernel, leveraging localized excitonic effects in the Hilbert space of crystalline systems to achieve significant data compression through singular value decomposition (SVD). We show that the subspace of non-zero singular values remains small even as the k-grid grows, ensuring computational tractability with extremely dense k-grids. This low-rank property enables at least 95% data compression and an order-of-magnitude speedup of TD-aGW calculations. Our approach avoids intensive training processes and eliminates time-accumulated errors, seen in previous approaches, providing a general framework for high-throughput, nonequilibrium simulation of light-driven dynamics in materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533948","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

Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction 用于有机分子光谱预测的预训练E(3)等变消息传递神经网络

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01698-z

Yuzhi Xu, Daqian Bian, Cheng-Wei Ju, Fanyu Zhao, Pujun Xie, Yuanqing Wang, Wei Hu, Zhenrong Sun, John Z. H. Zhang, Tong Zhu

{"title":"Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction","authors":"Yuzhi Xu, Daqian Bian, Cheng-Wei Ju, Fanyu Zhao, Pujun Xie, Yuanqing Wang, Wei Hu, Zhenrong Sun, John Z. H. Zhang, Tong Zhu","doi":"10.1038/s41524-025-01698-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01698-z","url":null,"abstract":"Fast and accurate spectral prediction plays a crucial role in molecular design within fields such as pharmaceutical and materials science. Nevertheless, predicting molecular spectra typically requires quantum chemistry calculations, posing significant challenges for fast predictions and high-throughput screening. In this paper, we propose an equivariant, fast, and robust model, named EnviroDetaNet, which integrates molecular environment information. EnviroDetaNet employs an E(3)-equivariant message-passing neural network combining intrinsic atomic properties, spatial features, and environmental information, allowing it to comprehensively capture both local and global molecular information. Compared to state-of-the-art machine learning models, EnviroDetaNet excels in various predictive tasks and maintains high accuracy even with a 50% reduction in training data, demonstrating strong generalization capabilities. Ablation studies confirm that molecular environment information is crucial for improving model stability and accuracy. EnviroDetaNet also shows outstanding performance in spectral predictions for complex molecular systems, making it a powerful tool for accelerating molecular discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533950","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 atomic forces from uncertainty-calibrated adversarial attacks 从不确定性校准的对抗性攻击中学习原子力

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-01 DOI: 10.1038/s41524-025-01703-5

Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson, Morten Ledum, Simen Reine, Sigbjørn Løland Bore

{"title":"Learning atomic forces from uncertainty-calibrated adversarial attacks","authors":"Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson, Morten Ledum, Simen Reine, Sigbjørn Løland Bore","doi":"10.1038/s41524-025-01703-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01703-5","url":null,"abstract":"Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs). While already providing great practical value, little is known about the actual prediction errors of MLIPs on adversarial structures and whether these errors can be controlled. We propose the Calibrated Adversarial Geometry Optimization (CAGO) algorithm to discover adversarial structures with user-assigned errors. Through uncertainty calibration, the estimated uncertainty of MLIPs is unified with real errors. By performing geometry optimization for calibrated uncertainty, we reach adversarial structures with the user-assigned target MLIP prediction error. Integrating with active learning pipelines, we benchmark CAGO, demonstrating stable MLIPs that systematically converge structural, dynamical, and thermodynamical properties for liquid water and water adsorption in a metal-organic framework within only hundreds of training structures, where previously many thousands were typically required.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"646 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533955","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