npj Computational Materials最新文献_第7页

Dynamical approach to realize room-temperature superconductivity in LaH10 实现LaH10室温超导性的动力学方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-14 DOI: 10.1038/s41524-025-01725-z

Chendi Xie, Adam D. Smith, Haoran Yan, Wei-Chih Chen, Yao Wang

引用次数: 0

Accelerating structure relaxation in chemically disordered materials with a chemistry-driven model 用化学驱动模型加速化学无序材料的结构松弛

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-14 DOI: 10.1038/s41524-025-01694-3

Liying An, Huan Ma, Jinjia Liu, Wenping Guo, Xiaodong Wen

引用次数: 0

A process-synergistic active learning framework for high-strength Al-Si alloys design 高强度铝硅合金设计的过程协同主动学习框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-14 DOI: 10.1038/s41524-025-01721-3

Jianming Cai, Mengxia Han, Xirui Yan, Yan Chen, Daoxiu Li, Kai Zhao, Dongqing Zhang, Kaiqi Hu, Heng Han Sua, Hieng Kiat Jun, Kewei Xie, Guiliang Liu, Xiangfa Liu, Sida Liu

{"title":"A process-synergistic active learning framework for high-strength Al-Si alloys design","authors":"Jianming Cai, Mengxia Han, Xirui Yan, Yan Chen, Daoxiu Li, Kai Zhao, Dongqing Zhang, Kaiqi Hu, Heng Han Sua, Hieng Kiat Jun, Kewei Xie, Guiliang Liu, Xiangfa Liu, Sida Liu","doi":"10.1038/s41524-025-01721-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01721-3","url":null,"abstract":"High-strength Al-Si alloys are important lightweight materials, but their optimal design is hindered by scarce-imbalance data, and complex compositional-process-property relationships. Traditional trial-and-error experimentation fails to explore this multi-dimensional design space, where processing routes (PRs) and composition must be co-optimized to achieve superior strength. This study introduces a process-synergistic active learning (PSAL) framework leveraging a conditional Wasserstein autoencoder (c-WAE) to enable the data-efficient design. By encoding PRs as conditional variables, the PSAL framework reveals exceptional synergistic effects across diverse PRs, significantly outperforming single-process approaches. The process-aware latent representation facilitates the efficient exploration of potential compositions across multi-PRs simultaneously. Through iterative active learning cycles integrating machine learning predictions with experimental validations, ultimate tensile strength is greatly improved: 459.8 MPa for gravity casting with T6 heat treatment within three iterations and 220.5 MPa for gravity casting with hot extrusion in a single iteration. This framework handles sparse datasets effectively, capturing complex process-composition-property relationships and establishing a new paradigm for accelerated multi-objective material design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"280 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630052","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

Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks 统一的物理热力学描述符通过学习二氧化碳吸附性质在金属有机框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-12 DOI: 10.1038/s41524-025-01700-8

Emily Lin, Yang Zhong, Gang Chen, Sili Deng

{"title":"Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks","authors":"Emily Lin, Yang Zhong, Gang Chen, Sili Deng","doi":"10.1038/s41524-025-01700-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01700-8","url":null,"abstract":"The large design space of metal-organic frameworks (MOFs) has prompted the utilization of deep learning to drive material design. Nonetheless, the prediction of key thermodynamic properties, such as heat of adsorption ((Delta {H}_{{rm{ads}}})), remains largely unexplored for CO2 adsorption in MOFs. Herein, we present IsothermNet, a high-throughput graph neural network designed to estimate uptake and (Delta {H}_{{rm{ads}}}) over 0–50 bars, enabling high-quality full isotherm reconstruction (PCC: 0.73–0.95 [uptake], 0.76–0.88 [(Delta {H}_{{rm{ads}}})]). We further bridged these adsorption properties to uptake behaviors (i.e., isotherm shapes/types) and structural information by performing detailed ablation studies to investigate the relative importance of local and global features in relation to predictive performance. This comparative analysis facilitated the discovery of a (1) physically-interpretable and (2) analytically-derived universal descriptor set capable of illustrating interdependencies between easily-computed, accessible textural information and extrinsic adsorption properties. When used cooperatively with IsothermNet, these descriptors enable efficient material screening, accelerating high-performance MOF discovery for CO2 capture.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613006","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-accuracy physical property prediction for pure organics via molecular representation learning: bridging data to discovery 通过分子表征学习对纯有机物进行高精度物理性质预测：连接数据与发现

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-11 DOI: 10.1038/s41524-025-01720-4

Qi Ou, Hongshuai Wang, Minyang Zhuang, Shangqian Chen, Lele Liu, Ning Wang, Zhifeng Gao

{"title":"High-accuracy physical property prediction for pure organics via molecular representation learning: bridging data to discovery","authors":"Qi Ou, Hongshuai Wang, Minyang Zhuang, Shangqian Chen, Lele Liu, Ning Wang, Zhifeng Gao","doi":"10.1038/s41524-025-01720-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01720-4","url":null,"abstract":"The escalating energy crisis has spurred extensive research into organic compounds for energy-efficient applications, taking advantage of their environmental friendliness, cost-effective synthesis, and adaptable molecular structures. Traditional trial-and-error methods for discovering highly functional organic compounds are expensive and time-consuming. We employed a 3D transformer-based molecular representation learning algorithm to create the Org-Mol pre-trained model, using 60 million semi-empirically optimized small organic molecule structures. After fine-tuning with public experimental data, the model can accurately predict various physical properties of pure organics, with test set R2 values exceeding 0.92. These fine-tuned models are used in high-throughput screening among millions of ester molecules to identify novel immersion coolants, resulting in the experimental validation of two promising candidates. This work not only demonstrates the potential of Org-Mol in predicting bulk properties for pure organic compounds but also paves the way for the rational and efficient development of ideal candidates for energy-saving materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"22 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611249","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

Breathing ferroelectricity induced topological valley states in kagome niobium halide monolayers 呼吸铁电诱导kagome卤化铌单层的拓扑谷态

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-11 DOI: 10.1038/s41524-025-01717-z

Kai-Qi Wang, Jun-Ding Zheng, Wen-Yi Tong, Chun-Gang Duan

{"title":"Breathing ferroelectricity induced topological valley states in kagome niobium halide monolayers","authors":"Kai-Qi Wang, Jun-Ding Zheng, Wen-Yi Tong, Chun-Gang Duan","doi":"10.1038/s41524-025-01717-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01717-z","url":null,"abstract":"Recently, kagome lattices have garnered significant attention for their diverse properties in topology, magnetism, and electron correlations. However, the exploration of breathing kagome, which exhibit dynamic breathing behavior, remains relatively scarce. Structural breathing introduces an additional degree of freedom that is anticipated to fine-tune the exotic characteristic. In this study, we employ a combination of the k(cdot)p model and first-principles calculations to explore how breathing ferroelectricity modulate valley states within niobium halide monolayer. Through the interplay of magnetoelectric coupling and the lock-in between breathing and ferroelectric, we demonstrate that a breathing process can achieve valley polarization reversal and generate multiple valley states, including topologically nontrivial ones. These state transformations couple to circularly-polarized optical responses and various valley Hall effects. Our results suggest that breathing kagome represent promising platform for studying the interplay among structure, charge, spin and valley degrees of freedom, a crucial step toward developing multifunctional devices.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"51 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603429","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

Global atomic structure optimization through machine-learning-enabled barrier circumvention in extra dimensions 通过机器学习实现额外维度障碍规避的全局原子结构优化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-10 DOI: 10.1038/s41524-025-01656-9

Casper Larsen, Sami Kaappa, Andreas Lynge Vishart, Thomas Bligaard, Karsten Wedel Jacobsen

引用次数: 0

Two-dimensional Weyl and type-III Dirac semimetals in BaCu monolayer and twisted α/β-BaCu/BN systems BaCu单层和扭曲α/β-BaCu/BN体系中的二维Weyl和iii型Dirac半金属

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-08 DOI: 10.1038/s41524-025-01716-0

Yiwei Liang, Xinyan Lin, Biao Wan, Yujin Jia, Yuting Qian, Dexi Shao, Huiyang Gou

{"title":"Two-dimensional Weyl and type-III Dirac semimetals in BaCu monolayer and twisted α/β-BaCu/BN systems","authors":"Yiwei Liang, Xinyan Lin, Biao Wan, Yujin Jia, Yuting Qian, Dexi Shao, Huiyang Gou","doi":"10.1038/s41524-025-01716-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01716-0","url":null,"abstract":"Two-dimensional (2D) topological insulators with symmetry-protected helical edge states have drawn significant interest. The recently synthesized layered 2D electride BaCu features a monolayer structure with intriguing band crossings near Fermi level and a low exfoliation energy. In this study, first-principles calculations combined with symmetry analysis reveal that the BaCu monolayer behaves as a 2D topological insulator (TI) nature. When integrated with a 30° twisted (sqrt{3}times sqrt{3}) hexagonal boron nitride (h-BN) supercell, the resulting twisted α/β-BaCu/BN heterobilayers exhibit 2D Weyl points and type-III Dirac points, respectively, demonstrating that twist angle can effectively modulate topological properties. Interestingly, ab initio molecular dynamics (AIMD) simulations reveal a spontaneous transition from the metastable β-BaCu/BN to α-BaCu/BN configuration, indicating a low energy barrier and highlighting the potential for property modulation, emphasizing the versatility of twisted structures for tuning topological states. This work establishes a robust platform for exploring twist-angle-induced topological electride states, broadening the scope for future investigations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144578354","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

First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions 具有扩展Hubbard相互作用的铁电钙钛矿氧化物介电性质的第一性原理研究

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-08 DOI: 10.1038/s41524-025-01711-5

Min Chul Choi, Wooil Yang, Young-Woo Son, Se Young Park

{"title":"First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions","authors":"Min Chul Choi, Wooil Yang, Young-Woo Son, Se Young Park","doi":"10.1038/s41524-025-01711-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01711-5","url":null,"abstract":"We investigate the atomic and electronic structures of ferroelectric perovskite oxides, BaTiO3, PbTiO3, LiNbO3, and BiFeO3 using ab initio extended Hubbard functionals (DFT + U + V), where on-site and inter-site Hubbard parameters are self-consistently determined via a pseudohybrid density functional by Agapito-Curtarolo-Buongiorno Nardelli. We compute band structures, ferroelectric distortions, polarization, Born effective charges, and switching barriers, compared with local density approximation, generalized gradient approximation (GGA), meta-GGA, and hybrid (HSE06) functionals. Results from DFT + U + V closely match experimental data, with the inter-site Hubbard terms significantly increasing band gaps, making closer alignment with GW results. The crucial role of the inter-site Coulomb interactions, restoring polar distortions suppressed by on-site U is discussed. Our approach yields accuracy comparable to HSE06 at over an order-of-magnitude lower computational cost. This combination of accuracy and efficiency makes DFT + U + V well suited for high-throughput calculations and properties such as bulk photovoltaic effect and band alignments of ferroelectric heterostructures.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"106 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144586801","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

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials 用于机器学习原子间势的材料不可知数据集的信息熵驱动生成

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-07 DOI: 10.1038/s41524-025-01602-9

Aparna P. A. Subramanyam, Danny Perez

{"title":"Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials","authors":"Aparna P. A. Subramanyam, Danny Perez","doi":"10.1038/s41524-025-01602-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01602-9","url":null,"abstract":"In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"37 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568731","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