npj Computational Materials最新文献_第7页

Inverse design of functional materials: a case study in thermoelectrics 功能材料的逆向设计：以热电学为例

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-31 DOI: 10.1038/s41524-026-02307-3

Xiangdong Wang, Jialin Ji, Yasong Wu, Xinru Bi, Hang Xiao, Lu Liu, Jinyang Xi, Lili Xi, Jiong Yang

{"title":"Inverse design of functional materials: a case study in thermoelectrics","authors":"Xiangdong Wang, Jialin Ji, Yasong Wu, Xinru Bi, Hang Xiao, Lu Liu, Jinyang Xi, Lili Xi, Jiong Yang","doi":"10.1038/s41524-026-02307-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02307-3","url":null,"abstract":"Artificial intelligence is transforming materials discovery from trial-and-error experimentation towards data driven design. Here we present a thermoelectric inverse-design (TEID) framework that integrates the MatHub-3d database, a generative model, and hierarchical screening across low-, medium-, and high-accuracy calculations. Trained on MatHub-3d, a crystal diffusion variational autoencoder generated 6400 candidate structures as the starting point of the workflow. Deduplication and stoichiometric/elemental constraints reduced this set to 1461 compositions. At the low-accuracy stage, pretrained machine-learning models rapidly narrowed the search space: M3GNet identified 1053 dynamically stable structures, and MEGNet returned 1005 thermodynamically viable compounds. At the density functional theory level, automated workflows comprising structural relaxation, convex hull analysis, band gap filtering, and deformation potential approximation electrical transport screening yielded 64 promising high-performance candidates. Phonon and third-order interatomic-force-constant calculations ultimately identified four dynamically stable and previously unreported compounds, DyTlTeSe, Li2In4Cu2Te8, Na3CaP3, and Si2Cu3AgS6 with lattice thermal conductivities of 0.67–1.72 W m−1 K−1. When combined with electrical transport calculations that include both deformation potential and polar optical phonon scattering, Na3CaP3 and Si2Cu3AgS6 achieve exceptional n-type performance, with ZT values of 1.59 and 2.24 at 700 K. These results demonstrate TEID as a generalizable framework for data-driven discovery and validation of novel functional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"164 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899511","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

Tailoring magnetocaloric effects in 2D transition-metal phthalocyanines via halogen substitution 通过卤素取代剪裁二维过渡金属酞菁中的磁热效应

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-31 DOI: 10.1038/s41524-026-02312-6

Peng Wang, Nanshu Liu, Hongkuan Yuan, Xue Jiang, Jijun Zhao

{"title":"Tailoring magnetocaloric effects in 2D transition-metal phthalocyanines via halogen substitution","authors":"Peng Wang, Nanshu Liu, Hongkuan Yuan, Xue Jiang, Jijun Zhao","doi":"10.1038/s41524-026-02312-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02312-6","url":null,"abstract":"The magnetocaloric effect (MCE) offers compact low-temperature refrigeration for quantum and energy technologies, but practical use requires affordable, synthesizable materials with high performance. Here, first-principles calculations and atomistic spin simulations identify halogen substitution as an effective route to tune two-dimensional transition-metal phthalocyanine frameworks (M-XPc; M = Cr, Mn; X = F, Cl, Br, I). Halogen atoms induce lattice expansion and charge redistribution, driving antiferromagnetic-to-ferromagnetic transitions and modifying magnetic anisotropy. This chemical control produces inverse, rotating, and conventional MCE within one rare-earth-free material family. Cr-BrPc shows a large inverse MCE, with a maximum magnetic entropy change of –9.04 µJ m−2 K−1 (–6.21 J kg−1 K−1) under a 5 T field change, and a rotating entropy change of 8.73 µJ m−2 K−1 (5.91 J kg−1 K−1). Mn-IPc exhibits enhanced conventional MCE near the hydrogen-liquefaction temperature range, reaching 5.47 µJ m−2 K−1 (2.88 J kg−1 K−1) and an adiabatic temperature change of 4.65 K. These results establish halogenated MPc frameworks as tunable rare-earth-free candidates for cryogenic magnetic refrigeration.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899510","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

ProvMind: provenance-grounded reasoning for materials synthesis ProvMind：基于来源的材料合成推理

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-31 DOI: 10.1038/s41524-026-02303-7

Yiming Zhang, Ryo Tamura, Masaya Kumagai, Koji Tsuda

{"title":"ProvMind: provenance-grounded reasoning for materials synthesis","authors":"Yiming Zhang, Ryo Tamura, Masaya Kumagai, Koji Tsuda","doi":"10.1038/s41524-026-02303-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02303-7","url":null,"abstract":"Materials process optimization requires reasoning over routes, conditions, tools and material-flow dependencies, yet most computational formulations flatten synthesis procedures into text or ordered steps. We introduce MatProcBench, a provenance-grounded benchmark constructed from literature-mined MatPROV graphs, to evaluate seven process-reasoning tasks spanning route continuity, step-level variable inference and global material-flow consistency under both same-split and shift-aware evaluation. Its strict dual-distribution-shift (dual-OOD) split simultaneously tests transfer to later publications and to a held-out material class. We further introduce ProvMind, a process-memory reasoning framework that retrieves analogous training processes, converts them into provenance-aware option-level compatibility scores, and uses a language model for constrained final decision making. ProvMind achieves 52.84% accuracy on the dual-OOD split, exceeding the aggregate accuracies of the evaluated prompting, retrieval-augmented and supervised fine-tuning baselines. These results show the potential of provenance-grounded process memory under controlled distribution shift, while complex route and joint-condition tasks remain challenging.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"51 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148899554","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

Complexity of disconnection dynamics during grain boundary migration 晶界迁移过程中断开动力学的复杂性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-29 DOI: 10.1038/s41524-026-02294-5

Inam Lalani, Ryan B. Sills

引用次数: 0

Domain wall dynamics in trilayer sliding ferroelectric γ-InSe 三层滑动铁电γ-InSe畴壁动力学

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-29 DOI: 10.1038/s41524-026-02296-3

Minzhi Dai, Xiaoyue Liu, Jianhua Ren, Yubai shi, Bingye Chen, Ri He, Xin Luo, Yue Zheng

{"title":"Domain wall dynamics in trilayer sliding ferroelectric γ-InSe","authors":"Minzhi Dai, Xiaoyue Liu, Jianhua Ren, Yubai shi, Bingye Chen, Ri He, Xin Luo, Yue Zheng","doi":"10.1038/s41524-026-02296-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02296-3","url":null,"abstract":"Sliding ferroelectricity in van der Waals layers presents a novel mechanism for polarization switching. It features an ultralow energy barrier and high endurance, both governed by domain wall (DW) motion. Compared to bilayer sliding ferroelectric systems, trilayer and multilayer architectures introduce multiple sliding interfaces, causing polarization switching to involve intermediate domain states and coupled DW dynamics rather than a simple single-interface process. Nonetheless, the realization of these multistate switching functionalities is strongly dependent on DWs dynamics, which become substantially more intricate in these layered architectures and are still poorly understood. Here, using trilayer γ-InSe as a minimal prototype of multilayer sliding ferroelectrics, we systematically investigate the static and dynamic properties of DWs by combining density functional theory calculations and machine-learning-assisted molecular dynamics simulations. The simulation results reveal that adjacent layers form DWs at distinct locations, separated by nonpolar domains situated between the upward- and downward-polarized ferroelectric domains, which markedly differs from the bilayer structure. Under external electric fields, polarization switching proceeded through distinct kinetic stages, with DW motion exhibiting a parabolic displacement-time relationship that transitioned from acceleration to DW velocity at higher fields. Notably, the collective motion of the DWs indicates the presence of significant inter-DW interactions. This work provides an atomistic picture of intermediate nonpolar domains and staged DW-mediated switching dynamics in trilayer sliding ferroelectric γ-InSe, providing microscopic insight into DW-mediated switching in multilayer sliding ferroelectrics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853524","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

Enhanced prediction of molecular properties using transfer learning on sigma profiles 在sigma剖面上使用迁移学习增强分子性质预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-29 DOI: 10.1038/s41524-026-02223-6

Tianzhixi Yin, Peiyuan Gao, Audrey Xu, Emily G. Saldanha

{"title":"Enhanced prediction of molecular properties using transfer learning on sigma profiles","authors":"Tianzhixi Yin, Peiyuan Gao, Audrey Xu, Emily G. Saldanha","doi":"10.1038/s41524-026-02223-6","DOIUrl":"https://doi.org/10.1038/s41524-026-02223-6","url":null,"abstract":"The performance of machine learning techniques for the prediction of a wide range of molecular properties has seen rapid improvements in recent years due to developments in both molecular representations and deep learning modeling techniques. Sigma profiles, which are computational descriptors representing the surface charge distribution of molecules, have shown promise as a molecular representation to support robust property prediction. Meanwhile, large-scale pretrained deep learning models based directly on molecular structure inputs, such as Uni-Mol, have demonstrated strong performance as general-purpose molecular representation learners. In this study, we seek to enhance the prediction of molecular properties by integrating information from sigma profiles with these advanced deep learning techniques. Our methodology involves fine-tuning the Uni-Mol model to accurately predict sigma profiles, which capture detailed molecular structural information important for determining molecular interactions. We then utilize transfer learning to apply the learned weights to predict specific molecular properties, replacing the final output layer to adapt to each new task. The results demonstrate improvements in predictive accuracy across various datasets, showcasing the effectiveness of combining sigma profiles with state-of-the-art machine learning models and demonstrating a path forward for leveraging theory-driven descriptor development to enhance large-scale data-driven molecular property modeling.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"56 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853526","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-scale mixture of experts model for cross-size structural prediction of Cu nanoparticles 铜纳米颗粒跨尺寸结构预测的多尺度混合专家模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-27 DOI: 10.1038/s41524-026-02280-x

Yunyu Zhang, Keith T. Butler, C. Richard A. Catlow

{"title":"A multi-scale mixture of experts model for cross-size structural prediction of Cu nanoparticles","authors":"Yunyu Zhang, Keith T. Butler, C. Richard A. Catlow","doi":"10.1038/s41524-026-02280-x","DOIUrl":"https://doi.org/10.1038/s41524-026-02280-x","url":null,"abstract":"Predicting structures and energetics of metallic nanoparticles across wide size ranges remains challenging because the balance of interaction scales changes rapidly with system size. We introduce a multi-scale Mixture-of-Experts (MoE) architecture for Cu clusters and nanoparticles that explicitly separates short-, medium-, and long-range interactions using three specialised machine-learning interatomic potential experts combined through a learnable size-conditioned gating network. The resulting MoE aggregates per-atom energies into a single conservative potential, ensuring forces are obtained as energy gradients and enabling stable molecular dynamics. Across mixed cluster-nanoparticle test sets, the MoE improves accuracy relative to both an off-the-shelf foundation potential and a finetuned single-expert baseline. Stress tests show that force errors remain comparatively stable across cluster sizes and that the model retains robust energetics under morphology out-of-distribution shifts quantified using a structural outlier score based on similarity measures. The learned gating weights further provide an interpretable, size-dependent decomposition of interaction scales. Finally, validation against linear-scaling density functional theory using the ONETEP code, together with finite-temperature molecular dynamics tests, demonstrates consistent energetics, stable force behaviour, and well-behaved dynamical trajectories, supporting the use of the model for efficient structural optimisation and configurational sampling across nanoparticle sizes.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853527","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

Grouper: symmetry-aware functional-group graph representations for generative exploration of chemical space 石斑鱼：化学空间生成探索的对称感知功能群图表示

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-27 DOI: 10.1038/s41524-026-02263-y

Kieran Nehil-Puleo, Nicholas C. Craven, Clare McCabe, Peter T. Cummings

{"title":"Grouper: symmetry-aware functional-group graph representations for generative exploration of chemical space","authors":"Kieran Nehil-Puleo, Nicholas C. Craven, Clare McCabe, Peter T. Cummings","doi":"10.1038/s41524-026-02263-y","DOIUrl":"https://doi.org/10.1038/s41524-026-02263-y","url":null,"abstract":"The vastness of chemical space holds immense potential for discovery, yet its systematic exploration remains computationally daunting. We mitigate this complexity by focusing on chemically meaningful differences captured by functional-group motifs and collapsing symmetry-equivalent configurations. We operationalize this principle with a symmetry-aware, hierarchical representation of molecules composed of functional groups. Combined with combinatorial and algebraic methods, this enables efficient generation and analysis of chemical spaces. By exploiting graph and group symmetry, we cut isomorphism checks by nearly 99% compared with naïve enumeration near the exhaustive limit. The resulting computational savings, together with scalable, parallel code, give access to previously untenable strategies such as exhaustive design, verified via Pólya theory. This framework ensures interoperability across simulation formats and data-driven models, enabling end-to-end molecular design (generation, characterization, and analysis). We demonstrate its utility in solubility optimization and polymer functionalization, opening pathways to synthetically meaningful, computationally tractable discovery pipelines.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"41 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853528","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

Target-driven discovery of metal hydrides using a high-throughput computational framework: from operational targets to sustainability assessment 使用高通量计算框架的目标驱动的金属氢化物发现：从操作目标到可持续性评估

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-26 DOI: 10.1038/s41524-026-02295-4

Renato Belli Strozi, Caroline Binde Stoco, Otávio Abreu Pedroso, Francisco Gil Coury, Betania Campello, Ricardo Floriano, Guilherme Zepon

{"title":"Target-driven discovery of metal hydrides using a high-throughput computational framework: from operational targets to sustainability assessment","authors":"Renato Belli Strozi, Caroline Binde Stoco, Otávio Abreu Pedroso, Francisco Gil Coury, Betania Campello, Ricardo Floriano, Guilherme Zepon","doi":"10.1038/s41524-026-02295-4","DOIUrl":"https://doi.org/10.1038/s41524-026-02295-4","url":null,"abstract":"Metal hydrides based on multi-principal element alloys (MPEAs) offer tunable hydrogen storage properties, but their vast compositional space poses significant design challenges. A high-throughput computational strategy is proposed to identify alloys under predefined operating conditions, demonstrated here for hydrogen gas separation. CALPHAD equilibrium calculations were performed in the Ti-V-Cr-Nb system using a PythonTM script with PanPython (PandatTM SDK). Single-phase body-centered cubic (BCC) alloys were identified and evaluated using a computational framework to predict pressure-composition-temperature (PCT) diagrams. Candidates were screened based on operational requirements and ranked using multi-criteria decision analysis (MCDA), considering economic, environmental, and societal indicators. The highest-ranked alloy, Ti20V12.5Cr20Nb47.5, was synthesized and characterized. Hydrogen storage measurements using a volumetric apparatus confirmed reversible hydrogen uptake of 1.2 H/M (1.68 wt.%) under the targeted operating conditions. This integrated framework enables the identification of hydrides operating under predefined conditions while supporting sustainability-driven decision-making, paving the way for accelerated discovery of hydrogen materials and the advancement of sustainable energy technologies.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"37 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853529","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 first-principles framework for predicting radiative and non-radiative processes in color centers 预测色心辐射和非辐射过程的统一第一性原理框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-08-25 DOI: 10.1038/s41524-026-02267-8

Sosuke Iwamoto, Masahiro Hara, Heiji Watanabe, Takuma Kobayashi

{"title":"Unified first-principles framework for predicting radiative and non-radiative processes in color centers","authors":"Sosuke Iwamoto, Masahiro Hara, Heiji Watanabe, Takuma Kobayashi","doi":"10.1038/s41524-026-02267-8","DOIUrl":"https://doi.org/10.1038/s41524-026-02267-8","url":null,"abstract":"Uncovering promising spin-qubit candidates among a myriad of semiconductor point defects requires high-throughput screening based on first-principles calculations. In practice, however, assessing color centers must incorporate phonon-related quantities such as phonon-sideband emission and non-radiative transition rates, which makes the calculations considerably cumbersome. In this paper, we present a method that streamlines the computational steps required to include phonon contributions to both radiative and non-radiative processes. In evaluating the non-radiative transition rate, we simplify the original summation-based expression and obtained a compact, tractable closed-form formula in the low-temperature limit, assuming the electron-phonon coupling matrix element to be constant. Furthermore, we show that perturbative energy evaluations at displaced atomic configurations can be replaced by a much simpler computation of energetics within the single-phonon-mode approximation. We applied the methodology proposed here to impurity-vacancy defects in 4H-SiC and demonstrated that it is highly versatile, allowing straightforward application to a broad range of material systems beyond those examined in this study.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"35 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148853495","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