npj Computational Materials最新文献_第2页

General-purpose LLMs as constrained crystal composition generators 作为约束晶体成分发生器的通用llm

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-09 DOI: 10.1038/s41524-026-02339-9

Hedda Oschinski,Maximilian L. Ach,Konstantin S. Jakob,Christian Carbogno,Karsten Reuter

{"title":"General-purpose LLMs as constrained crystal composition generators","authors":"Hedda Oschinski,Maximilian L. Ach,Konstantin S. Jakob,Christian Carbogno,Karsten Reuter","doi":"10.1038/s41524-026-02339-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02339-9","url":null,"abstract":"Abstract The targeted discovery of inorganic materials remains challenging due to the vastness of compositional design spaces and the high cost of exhaustive screening. Task-specific generative artificial intelligence represents a particularly efficient alternative to screening, yet demands tedious collection of training data before providing real benefit. General-purpose large language models (LLMs) have recently shown tremendous potential for the targeted generation of single, optimal materials compositions without the need for task-specific fine-tuning. However, it is unclear whether LLMs generally pose an advantage compared to specialized generative models, in particular in large design spaces. Here, we demonstrate that such models are capable of covering entire regions of the targeted property space effectively and systematically. Using Elpasolite materials as an established benchmark for generative tasks in large chemical spaces, we find that an iterative prompt-and-response framework is able to recover on average 96% of all low-energy Elpasolites in the target region. This performance, driven mainly by iterative in-context learning, surpasses the generative abilities of previous, task-specific models. Our results establish general-purpose LLMs as flexible and accessible components for inverse materials design workflows.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"82 1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151182","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

SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion 考虑量子核运动的有限温度下基于sscha的演化晶体结构预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-09 DOI: 10.1038/s41524-026-02333-1

Daniil Poletaev,Artem R. Oganov

{"title":"SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion","authors":"Daniil Poletaev,Artem R. Oganov","doi":"10.1038/s41524-026-02333-1","DOIUrl":"https://doi.org/10.1038/s41524-026-02333-1","url":null,"abstract":"Abstract Reliable crystal structure prediction (CSP) at finite temperatures, including quantum anharmonic effects, remains challenging but is particularly important for systems containing light atoms such as superconducting hydrides. Here, we integrate machine-learned interatomic potentials (MLIPs) with the stochastic self-consistent harmonic approximation (SSCHA) to enable evolutionary CSP on the quantum anharmonic free-energy landscape. Using LaH 10 as a test case, we compare three approaches: (i) active-learning MLIPs (AL-MLIPs) trained on the fly, (ii) universal MLIPs (uMLIPs), and (iii) temperature-dependent effective potentials (TDEPs) trained on SSCHA ensemble data. AL-MLIPs correctly predict the cubic Fm $$bar{3}$$ 3 ¯ m phase but require thermodynamic perturbation theory corrections for consistent results. The foundation uMLIP Mattersim-5m enables SSCHA-based CSP without per-structure training, but fine-tuning is required for higher accuracy. Temperature-dependent effective potentials trained on SSCHA data enable accurate and very efficient CSP for large unit cells on the quantum anharmonic free-energy landscape. We demonstrate that quantum anharmonicity simplifies the free-energy landscape and is essential for the correct energy ranking of LaH 10 structures.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151332","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 structure–spectrum mapping via manifold learning in amorphous In–Ga–Zn oxide 基于流形学习的非晶in - ga - zn氧化物可解释的结构光谱映射

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-08 DOI: 10.1038/s41524-026-02356-8

Taeyeon Kim,Kihyun Lee,Sol Lee,Aloysius Soon,Haneul Jang,Tae Young Oh,Woosun Jang,Kwanpyo Kim

引用次数: 0

A method for the automatic generation of a minimal basis set of structural templates for material phase-space exploration 一种用于材料相空间探索的结构模板最小基集的自动生成方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-08 DOI: 10.1038/s41524-026-02310-8

Caja Annweiler,Alessandro Serafini,Simone Di Cataldo,Maurits W. Haverkort,Lilia Boeri

{"title":"A method for the automatic generation of a minimal basis set of structural templates for material phase-space exploration","authors":"Caja Annweiler,Alessandro Serafini,Simone Di Cataldo,Maurits W. Haverkort,Lilia Boeri","doi":"10.1038/s41524-026-02310-8","DOIUrl":"https://doi.org/10.1038/s41524-026-02310-8","url":null,"abstract":"Abstract We present a method for efficiently predicting approximate binary convex hulls through the automatic generation of a minimal basis set of representative structural templates. The approach is based on the assumption that chemical space can be partitioned into a limited number of regions with similar bonding characteristics, such that a compact set of templates can capture the key structural and energetic trends across a wide range of compositions. Templates are selected via a data-driven algorithm informed by evolutionary structure searches, and subsequently reused to estimate formation enthalpies across diverse systems using limited DFT relaxations. We validate the method with a comprehensive benchmark dataset of 105 binary element pairs. Convex hulls predicted by our method are compared to evolutionary algorithm predictions using multiple metrics, including composition overlap, hull depth error, structural agreement. When targeting an average enthalpy error of 0.1 eV/atom, sufficient for high-throughput thermodynamic screening—the method achieves a 15-fold reduction in the number of DFT relaxations while maintaining 87% accuracy in identifying stable compositions. The approach is designed for scalability and applications in high-throughput exploration of complex material spaces.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151186","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

Fragment-based representation learning for polymers and heterogeneous formulations 聚合物和异质配方的基于片段的表示学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-08 DOI: 10.1038/s41524-026-02357-7

Jianxin He,Subhamoy Mahajan,Nathaniel Straight,Ying Li

引用次数: 0

Interface-controlled phase stability in polymorphic HfO2 revealed by machine-learning atomistic simulations 基于机器学习原子模拟的多晶HfO2界面控制相稳定性研究

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-07 DOI: 10.1038/s41524-026-02353-x

Xudong Zhu,Junhong Li,Lixin He

引用次数: 0

Targeted fine-tuning of machine-learning interatomic potentials for phonons and phase transitions 声子和相变的机器学习原子间势的定向微调

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-07 DOI: 10.1038/s41524-026-02338-w

Jonas Grandel,Philipp Benner,Janine George

{"title":"Targeted fine-tuning of machine-learning interatomic potentials for phonons and phase transitions","authors":"Jonas Grandel,Philipp Benner,Janine George","doi":"10.1038/s41524-026-02338-w","DOIUrl":"https://doi.org/10.1038/s41524-026-02338-w","url":null,"abstract":"Abstract Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale and long-time modeling of materials systems. Here, we investigate how different fine-tuning strategies affect the prediction of harmonic phonon band structures, thermal and elastic properties, and the potential-energy surface along unstable phonon modes. We show that substantial improvements can be achieved with very limited additional data, with as few as 10 material-specific training structures. We investigate targeted L 2 -SP ( L 2 -TSP) for this application, combining regularization toward the pre-trained model with selective fine-tuning of the first two representation-building layers, alongside transfer learning and multihead fine-tuning, with an additional comparison to low-rank LoRA for phonon prediction. Across 53 materials, L 2 -TSP achieves the most consistent overall performance, reducing phonon errors while also improving predictions of thermodynamic and elastic properties. Importantly, models with similar harmonic phonon accuracy can differ substantially in their prediction of dynamical instabilities and phase-transition behavior. Among the investigated approaches, L 2 -TSP shows the best overall agreement in dynamical-stability classification and in recovering the associated DFT reference phases, demonstrating improved generalization beyond the fine-tuning region. We implement L 2 -SP and L 2 -TSP in Equitrain , a model-agnostic software package for training and fine-tuning machine-learning interatomic potentials that can be applied beyond the MACE architecture considered here.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091814","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

Phonon-limited electron mobility in polar semiconductors from first principles: the role of the effective mass 从第一性原理看极性半导体中声子限制电子迁移率：有效质量的作用

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-05 DOI: 10.1038/s41524-026-02332-2

Mohammad Dehghani, Dominic Waldhoer, Angus Gentles, Pedram Khakbaz, Rainer Minixhofer, Michael Waltl

{"title":"Phonon-limited electron mobility in polar semiconductors from first principles: the role of the effective mass","authors":"Mohammad Dehghani, Dominic Waldhoer, Angus Gentles, Pedram Khakbaz, Rainer Minixhofer, Michael Waltl","doi":"10.1038/s41524-026-02332-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02332-2","url":null,"abstract":"We present a comprehensive ab initio investigation of phonon-limited electron transport in the high-mobility polar semiconductors GaAs, InAs, and InSb. Our approach is based on the DFT+U method, combined with an iterative solution of the linearized Boltzmann transport equation using the Wannier interpolation technique. We show that this framework allows for systematic refinement of the electronic band structure and phonon dispersion, leading to a consistent description of phonon-limited transport properties. In particular, by systematically tuning the conduction band characteristics through controlled variation of the Hubbard parameters, we establish the electron effective mass as a key parameter governing drift and Hall mobilities. Our results reveal a clear and systematic dependence of mobility on effective mass, including deviations from conventional power-law behavior arising from band nonparabolicity. The resulting mobilities are consistent with available experimental trends and with previously reported theoretical values obtained either from computationally demanding GW calculations or from established semi-empirical methods. These findings provide a practical first-principles framework for analysing and predicting phonon-limited mobility trends and fundamental insight into the interplay between band structure and carrier transport, with implications for the design of high-mobility semiconductors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087612","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

Reproducible capillary fluctuation analysis of solid-liquid interfaces for stiffness and anisotropy calculations 固液界面刚度和各向异性计算的可重复毛细管波动分析

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-05 DOI: 10.1038/s41524-026-02350-0

Kai Liu, Douglas E. Spearot, Damien Tourret

{"title":"Reproducible capillary fluctuation analysis of solid-liquid interfaces for stiffness and anisotropy calculations","authors":"Kai Liu, Douglas E. Spearot, Damien Tourret","doi":"10.1038/s41524-026-02350-0","DOIUrl":"https://doi.org/10.1038/s41524-026-02350-0","url":null,"abstract":"The capillary fluctuation method (CFM) is widely used to compute solid–liquid interfacial properties from atomistic simulations, but its accuracy depends on choices in interface construction, wave-vector selection, sampling, and simulation geometry. Here, we develop a diagnostics-driven workflow for reproducible CFM calculations using pure Al as a representative system. Employing both ribbon models and thick two-dimensional references, we show that the apparent linearity of the fluctuation spectrum alone does not guarantee reliable estimates of stiffness or anisotropy. Instead, a reliable CFM analysis requires a fitting window consistent with both temporal sampling and continuum capillary-wave assumptions, systematic sensitivity tests of the interface identification procedure, explicit propagation of replica variability, and independent verification of model-thickness convergence. We further propose a practical thickness-selection rule based on coexistence-temperature consistency, which enables control of finite-size effects while retaining the substantial computational efficiency of ribbon geometries. By making the main sources of uncertainty explicit and diagnosable, the proposed workflow improves the reliability of the CFM as a quantitative tool and provides a foundation for its broader application to complex solid–liquid interfaces.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087874","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

Unsupervised machine learning for automated crystal orientation mapping on noisy 4D-STEM data 基于噪声4D-STEM数据的无监督机器学习自动晶体取向映射

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-03 DOI: 10.1038/s41524-026-02337-x

Kai Kamijo, Motoki Shiga, Shusuke Kanomi, Tomohiro Miyata, Hiroshi Jinnai

{"title":"Unsupervised machine learning for automated crystal orientation mapping on noisy 4D-STEM data","authors":"Kai Kamijo, Motoki Shiga, Shusuke Kanomi, Tomohiro Miyata, Hiroshi Jinnai","doi":"10.1038/s41524-026-02337-x","DOIUrl":"https://doi.org/10.1038/s41524-026-02337-x","url":null,"abstract":"Four-dimensional scanning transmission electron microscopy (4D-STEM) is a high-throughput measurement used to comprehensively acquire diffraction patterns across a region of interest in a specimen. While the measurement is useful for identifying crystallographic microstructures, the data analysis requires significant effort owing to the complexity and high dimensionality of the data. Furthermore, the analysis of low signal-to-noise data from beam-sensitive materials such as polymers is particularly challenging. This study proposes an automatic analysis pipeline that leverages denoising and unsupervised crystallographic analysis for 4D-STEM data. The denoising method is based on a self-supervised approach that eliminates the need for clean training images. The developed method is extended to 4D-STEM data by utilizing diffraction patterns observed at neighboring probe positions. The subsequent unsupervised analysis is designed to be invariant to the crystal in-plane rotations, enabling the effective identification of crystal components. The efficacy of the proposed pipeline is evaluated using both synthetic and experimental 4D-STEM data. Quantitative evaluation for synthetic data across various noise levels demonstrates the robustness of our approach. The results from the experimental data show good agreement with expert analysis in the previous study; furthermore, the pipeline identifies minor crystallographic components that were previously overlooked. These results demonstrate that the pipeline enables robust and automated analysis with significantly reduced analysis cost.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087875","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