npj Computational Materials最新文献_第3页

Trajectory-dependent electronic stopping in simulations of self-ion ranges in elemental semiconductors 元素半导体中自离子范围模拟中的轨迹依赖电子停止

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-03 DOI: 10.1038/s41524-026-02347-9

Rafael Nuñez-Palacio, Glen P. Kiely, Artur Tamm, Andrea E. Sand

{"title":"Trajectory-dependent electronic stopping in simulations of self-ion ranges in elemental semiconductors","authors":"Rafael Nuñez-Palacio, Glen P. Kiely, Artur Tamm, Andrea E. Sand","doi":"10.1038/s41524-026-02347-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02347-9","url":null,"abstract":"Characterizing how materials respond to energetic-particle irradiation is fundamental to a wide range of semiconductor processes. However, the deceleration of energetic particles in solids remains an active area of research due to the complex nature of energy exchange between ionic and electronic systems. Recently, the advent of two-temperature molecular dynamics models has enabled more realistic simulations of this highly anisotropic deceleration process. In this work, we present a parametrization of the unified two-temperature model for self-ions in elemental semiconductor crystals that accurately reproduces the electronic stopping values obtained from real-time time-dependent density functional theory (rt-TDDFT). This includes channeled and incommensurate trajectories, as well as collision-like interactions. Using this model, we obtain ion range profiles that closely match available experimental data. We present penetration profiles for principal channeling directions and random trajectories. In the process, we characterize rt-TDDFT electronic stopping in germanium and diamond and extend existing results for silicon.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087876","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

Systematic fine-tuning of MACE interatomic potentials for catalysis MACE催化原子间电位的系统微调

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-02 DOI: 10.1038/s41524-026-02335-z

Nima Karimitari, Jacob Clary, Derek Vigil-Fowler, Ravishankar Sundararaman, Gábor Csányi, Christopher Sutton

{"title":"Systematic fine-tuning of MACE interatomic potentials for catalysis","authors":"Nima Karimitari, Jacob Clary, Derek Vigil-Fowler, Ravishankar Sundararaman, Gábor Csányi, Christopher Sutton","doi":"10.1038/s41524-026-02335-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02335-z","url":null,"abstract":"Once trained, machine-learned interatomic potentials (MLIPs) offer a fast and accurate way to investigate catalytic reaction pathways, but their performance depends strongly on training-set design. We compared nine MLIPs developed using from-scratch (FS) training and fine-tuning (FT) of foundation models. The models were evaluated on reaction energies (Er) and activation barriers (Ea) for 141 reactions, including CO2RR to C2/C3 products, propane dehydrogenation, hydrogen intercalation on Pd, and out-of-distribution OER on metal oxides. The FS models containing 5–10% perturbed, high-energy configurations from molecular dynamics or contour exploration reduced errors by more than twofold relative to models trained only on relaxation trajectories. However, FT models were less sensitive to sampling and generalized better to out-of-distribution chemistry. For example, the FT model fine-tuned on metallic catalysts achieved an MAE of 0.30 eV for OER on iridium oxide polymorphs, while the FT model fine-tuned on metal-oxides gave an Ea of 0.19 eV for CO2RR on Cu. The best model, fine-tuned on 49,860 configurations, performed consistently across metallic and oxide catalysts and screened 90,781 bimetallic alloys with a reaction-energy MAE of 0.15 eV, including unseen high-index surfaces. These results identify the necessary training set configurations for training transferable catalytic MLIPs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"39 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087877","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 bond reconfiguration drives crack-tip carbon migration to strengthen iron grain boundaries 动态键重配置驱动裂纹尖端碳迁移，强化铁晶界

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-02 DOI: 10.1038/s41524-026-02352-y

Kazuma Ito, Takashi Otaki, Yuta Yoshimoto, Naoki Matsumura

{"title":"Dynamic bond reconfiguration drives crack-tip carbon migration to strengthen iron grain boundaries","authors":"Kazuma Ito, Takashi Otaki, Yuta Yoshimoto, Naoki Matsumura","doi":"10.1038/s41524-026-02352-y","DOIUrl":"https://doi.org/10.1038/s41524-026-02352-y","url":null,"abstract":"Developing stronger alloys is essential for structural applications, but their reliability is limited by brittle cracking along general grain boundaries (GGBs). Solute segregation can control grain-boundary fracture, yet its effects are typically interpreted in terms of static cohesion changes before and after interfacial separation. How trace solutes modify moving-crack resistance at GGBs remains unclear because realistic segregation and crack propagation are difficult to treat atomistically. Here, we develop a fast and transferable Fe–C machine-learning interatomic potential. Using simulations of experimentally consistent carbon-segregated α-Fe GGBs, we show that trace carbon markedly increases crack-growth resistance, consistent with observed transition from intergranular fracture to cleavage. Carbon follows the moving crack tip through repeated breaking and reforming of Fe–C bonds, enriching the crack-tip region. We term this process dynamic bond reconfiguration (DBR). DBR dynamically amplifies static cohesive strengthening, revealing dynamic solute–crack-tip interactions as a framework for controlling brittle fracture in alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"105 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087602","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

Antiferroelectric phase-field simulations via scale-invariant physics-informed neural operators 基于尺度不变物理信息的神经算子的反铁电相场模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-01 DOI: 10.1038/s41524-026-02344-y

Dilshod Durdiev, Bai-Xiang Xu

{"title":"Antiferroelectric phase-field simulations via scale-invariant physics-informed neural operators","authors":"Dilshod Durdiev, Bai-Xiang Xu","doi":"10.1038/s41524-026-02344-y","DOIUrl":"https://doi.org/10.1038/s41524-026-02344-y","url":null,"abstract":"Optimizing antiferroelectric (AFE) energy-storage microstructures via phase-field modeling is computationally prohibitive due to immense spatial and temporal constraints. First, accurately capturing the highly frustrated commensurate and incommensurate AFE phases requires exceptionally fine spatial resolutions to resolve sharp, high-frequency antiparallel dipole gradients. Second, simulating path-dependent double hysteresis loops requires quasi-static loading, where time-dependent evolution equations must be rigorously integrated at every electric-field increment until the macroscopic polarization reaches thermodynamic equilibrium. Together, these requirements render high-throughput simulations of polycrystalline structures practically unfeasible. To overcome this, we introduce a physics-informed multigrid neural operator (PI-MgNO) as a highly scalable, autoregressive surrogate model. Constrained by the physical Landau free energy, PI-MgNO accurately tracks AFE-to-FE domain evolution without catastrophic integration drift. This physical regularization imparts a distinct thermodynamic error-healing mechanism, bounding spatial phase-shift errors during coercive switching and rapidly forcing the system into stable energy minima. Rigorous evaluations demonstrate exceptional scale-invariance, effectively bypassing the quasi–static relaxation bottleneck and reducing simulation times from hours to seconds while maintaining R2 > 0.96.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087603","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

Large language model-driven inverse design of acoustic valley-Hall insulators 基于大语言模型的声谷-霍尔绝缘体反设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-30 DOI: 10.1038/s41524-026-02349-7

Yinchu Wang, Xiaofeng Li, Silin Wang, Jiashuo Xu, Liang Gao, Mi Xiao

{"title":"Large language model-driven inverse design of acoustic valley-Hall insulators","authors":"Yinchu Wang, Xiaofeng Li, Silin Wang, Jiashuo Xu, Liang Gao, Mi Xiao","doi":"10.1038/s41524-026-02349-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02349-7","url":null,"abstract":"Acoustic valley-Hall insulators (AVHIs) are an emerging class of acoustic topological materials that enable robust directional wave transport and show promise for applications in communication, sensing and wave manipulation. This study presents a large language model (LLM)-driven inverse design framework consisting of two collaborative agents. The Designer-LLM is fine-tuned on an AVHI database generated through reinforcement learning to perform bandgap-targeted inverse design. The Manager-LLM coordinates task planning, semantic parsing, simulation tool invocation and multimodal result generation. With this framework, multimodal design outputs, including structural parameters, band structures, eigenmode fields, and manufacturable model files, can be generated according to concise design requirements. Bandgap error analysis on test sets indicates that the fine-tuned Designer-LLM generates AVHI structures with reliable bandgap-matching accuracy. Overall, this study proposes a modular LLM-agent framework for the inverse design of AVHIs. The framework integrates semantic interaction, AVHI inverse design, numerical verification, and multimodal result generation into a unified workflow and can provide a useful reference for future extensions to other acoustic metamaterial design tasks.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087605","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

Predicting moiré physics from local proximity effects in van der Waals heterostructures 从范德华异质结构的局部邻近效应预测摩尔物理

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-29 DOI: 10.1038/s41524-026-02346-w

Lukas Cvitkovich, Klaus Zollner, Jaroslav Fabian

引用次数: 0

Machine learning-enabled prediction and interpretation of spectroscopic properties in Nd and Y co-doped fluorite platform via local coordination engineering 基于局部配位工程的Nd和Y共掺杂萤石平台光谱特性的机器学习预测和解释

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-29 DOI: 10.1038/s41524-026-02234-3

Hao Wu, Jun-Yang Liu, Yan-Yan Xue, Zhen Zhang, Da-Peng Jiang, Hua-Min Kou, Jun Xu, Feng-Kai Ma, Liang-Bi Su

{"title":"Machine learning-enabled prediction and interpretation of spectroscopic properties in Nd and Y co-doped fluorite platform via local coordination engineering","authors":"Hao Wu, Jun-Yang Liu, Yan-Yan Xue, Zhen Zhang, Da-Peng Jiang, Hua-Min Kou, Jun Xu, Feng-Kai Ma, Liang-Bi Su","doi":"10.1038/s41524-026-02234-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02234-3","url":null,"abstract":"The design of advanced laser materials demands precise control over the relationship between local structure and spectroscopic performance. However, conventional approaches struggle to quantitatively link complex coordination environments with spectral properties in rare-earth-doped systems, particularly for correlated 4f-electron states. Herein, we present a machine learning-enabled compositional optimization strategy for tailoring the spectral characteristics of neodymium and yttrium-co-doped fluorites, a platform featuring a wealth of structures and spectroscopic properties, via local coordination engineering. By integrating experimental spectroscopy with structural data, we train interpretable models—including Linear Regression (LR), Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Extreme Gradient Boosting (XGBoost)—to predict key spectroscopic parameters from the relative fractions of three dominant polyhedral motifs. The resultant models reveal, through Shapley value analysis, distinct roles of each structural unit in governing absorption, emission, and lifetime behavior. This data-driven framework not only overcomes limitations of traditional Density Functional Theory (DFT) in treating localized 4f states but also provides a scalable pathway for the intelligent compositional optimization of broadband, high-repetition-rate laser materials. This work highlights the effectiveness of combining local structural descriptors with interpretable machine learning to elucidate structure–property relationships in rare-earth-doped fluorite systems, providing guidance for the design of advanced laser materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087604","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

Interpretable substructure-based screening of multi-property polymer dielectrics with prompt-ready rules for rational design 基于可解释子结构的多性能聚合物电介质筛选及其合理设计规则

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-28 DOI: 10.1038/s41524-026-02345-x

Weihao Wang, Haoou Ruan, Keith T. Butler, Akiko Kumada, Masahiro Sato

{"title":"Interpretable substructure-based screening of multi-property polymer dielectrics with prompt-ready rules for rational design","authors":"Weihao Wang, Haoou Ruan, Keith T. Butler, Akiko Kumada, Masahiro Sato","doi":"10.1038/s41524-026-02345-x","DOIUrl":"https://doi.org/10.1038/s41524-026-02345-x","url":null,"abstract":"High-performance polymer dielectrics remain difficult to discover because transparent connections between molecular substructures and macroscopic properties are lacking. Existing machine-learning approaches often rely on black-box representations that obscure the chemical origins of thermal and electrical behavior. Here, we introduce sliding threshold correlation analysis (STCA), a white-box framework that identifies threshold-dependent substructure combinations governing polymer performance. By scanning property thresholds, STCA reveals hierarchical motifs that remain predictive across thermal (glass-transition temperature, melting temperature, decomposition temperature, and thermal conductivity) and electrical (electrical conductivity and alternating-current dielectric constant) domains, enabling unified multi-property screening. STCA shows strong performance relative to common interpretability baselines in discriminating high- and low-performance regimes, while its threshold-continuous signed rule families remain applicable in selected stringent screening conditions. The extracted combinations align with polymer physics, capturing rigidity-driven thermal stability and limited delocalization in insulating systems. To demonstrate practical utility, we convert these combinations into chemically grounded prompts for a commercial large-language model, yielding synthetically accessible monomers with the anticipated characteristics. DFT and molecular-dynamics evaluations support representative thermal and electrical trends. STCA therefore provides a transparent framework for rule-based polymer screening and prompt-ready candidate generation within the linear-homopolymer repeat-unit domain.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"8 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087606","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

Molecular modelling of high-entropy alloys: from quantum to atomistic, and machine learning 高熵合金的分子建模：从量子到原子，以及机器学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-26 DOI: 10.1038/s41524-026-02319-z

Michele Pellegrino,Mokhtar Ganjali Koli,Fabiano Tarulli,Joseph E. Arnold,Dumitru Mitrica,Luca Bergamasco,Gonzalo García Fuentes,Radu Piticescu,Beñat Gurrutxaga-Lerma,Matteo Fasano

引用次数: 0

Benchmarking model-based design of experiment approaches with a pharmaceutical crystallisation emulator 基于基准模型的药物结晶模拟器实验方法设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-09-26 DOI: 10.1038/s41524-026-02336-y

Thomas Pickles,Kakasaheb Nandiwale,Bhavik A. Mehta,Sammy Sambu,Jonathan Moores,Alastair J. Florence,Cameron J. Brown

引用次数: 0