npj Computational Materials最新文献

Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations 通过机器学习引导相场模拟导航金属离子电池的化学设计空间

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-24 DOI: 10.1038/s41524-025-01735-x

Hamed Taghavian, Viktor Vanoppen, Erik Berg, Peter Broqvist, Jens Sjölund

引用次数: 0

Design principles for density functionals using a linear expansion 使用线性展开的密度泛函的设计原则

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-22 DOI: 10.1038/s41524-025-01712-4

Ayoub Aouina, Matteo Gatti, Lucia Reining

引用次数: 0

Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine 有限角度x射线纳米层析成像与机器学习支持迭代重建引擎

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-19 DOI: 10.1038/s41524-025-01724-0

Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan

{"title":"Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine","authors":"Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan","doi":"10.1038/s41524-025-01724-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01724-0","url":null,"abstract":"A long-standing challenge in tomography is the ‘missing wedge’ problem, which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints. This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image. To tackle this challenge, we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine, which integrates a convolutional neural network (CNN) with perceptional knowledge as a smart regularizer into an iterative solving engine. We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains, thereby achieving a physically coherent and visually enhanced result. We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques. All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail. Notably, it also improves the reconstruction in case of sparse projections, despite the network not being specifically trained for that. This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664632","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

Multiscale, mechanistic modeling of cesium transport in silicon carbide for TRISO fuel performance prediction 用于TRISO燃料性能预测的碳化硅中铯输运的多尺度、机制建模

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-19 DOI: 10.1038/s41524-025-01734-y

Pierre-Clément A. Simon, Jia-Hong Ke, Chao Jiang, Larry K. Aagesen, Wen Jiang, Stephen Novascone

{"title":"Multiscale, mechanistic modeling of cesium transport in silicon carbide for TRISO fuel performance prediction","authors":"Pierre-Clément A. Simon, Jia-Hong Ke, Chao Jiang, Larry K. Aagesen, Wen Jiang, Stephen Novascone","doi":"10.1038/s41524-025-01734-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01734-y","url":null,"abstract":"Understanding cesium (Cs) transport in TRistructural ISOtropic (TRISO) particle fuel is crucial for predicting fission product release in high-temperature reactors. However, current challenges include significant scatter in diffusivity data and unexplained temperature-dependent diffusion regimes in the silicon carbide layer. This study addresses these challenges by developing a multiscale, mechanistic Cs transport model integrating atomistic simulations and phase field modeling. Our model quantifies temperature and grain size effects on Cs diffusivity, attributing experimentally observed regimes to a transition from bulk-dominated diffusivity at high temperatures to grain boundary-dominated diffusivity at lower temperatures. The model, validated against diffusion measurements and advanced gas reactor (AGR)-1 and AGR-2 post-irradiation fission product release data, enhances the predictive capability of the BISON fuel performance code. This study advances our understanding of Cs release from TRISO particles and its dependence on temperature and silicon carbide grain size, with implications for the safety and efficiency of high-temperature nuclear reactors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664633","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

Coupled lattice-charge-magnetic fluctuations for nonlocal flux mediated pairing in cuprate superconductors 铜超导体中非局域磁介导对的晶格-电荷-磁耦合波动

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-19 DOI: 10.1038/s41524-025-01697-0

Xi Chen, Yuchuang Cao, Jianghui Pan, Jiahao Dong, Changkai Luo, Xin Li

引用次数: 0

SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction SLM-MATRIX：一个多智能体轨迹推理和验证框架，用于增强材料数据提取中的语言模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-19 DOI: 10.1038/s41524-025-01719-x

Xin Li, Zhixuan Huang, Shu Quan, Cheng Peng, Xiaoming Ma

{"title":"SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction","authors":"Xin Li, Zhixuan Huang, Shu Quan, Cheng Peng, Xiaoming Ma","doi":"10.1038/s41524-025-01719-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01719-x","url":null,"abstract":"Small Language Models offer an efficient alternative for structured information extraction. We present SLM-MATRIX, a multi-path collaborative reasoning and verification framework based on SLMs, designed to extract material names, numerical values, and physical units from materials science literature. The framework integrates three complementary reasoning paths: a multi-agent collaborative path, a generator–discriminator path, and a dual cross-verification path. SLM-MATRIX achieves an accuracy of 92.85% on the BulkModulus dataset and reaches 77.68% accuracy on the MatSynTriplet dataset, both outperforming conventional methods and single-path models. Moreover, experiments on general reasoning benchmarks such as GSM8K and SVAMP validate the framework’s strong generalization capability. Ablation studies evaluate the effects of agent number, Mixture-of-Agents (MoA) depth, and discriminator design on overall performance. Overall, SLM-MATRIX presents an effective approach for high-quality material information extraction in resource-constrained and offers new insights into structured scientific text understanding tasks.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664621","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

Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning 基于冻结迁移学习的材料原子间势的微调基础模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-18 DOI: 10.1038/s41524-025-01727-x

Mariia Radova, Wojciech G. Stark, Connor S. Allen, Reinhard J. Maurer, Albert P. Bartók

{"title":"Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning","authors":"Mariia Radova, Wojciech G. Stark, Connor S. Allen, Reinhard J. Maurer, Albert P. Bartók","doi":"10.1038/s41524-025-01727-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01727-x","url":null,"abstract":"Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training data set remains a challenge, often requiring thousands of first-principles calculations to achieve high accuracy. Foundation models have started to emerge with the ambition to create universally applicable potentials across a wide range of materials. While foundation models can be robust and transferable, they do not yet achieve the accuracy required to predict reaction barriers, phase transitions, and material stability. This work demonstrates that foundation model potentials can reach chemical accuracy when fine-tuned using transfer learning with partially frozen weights and biases. For two challenging datasets on reactive chemistry at surfaces and stability and elastic properties of tertiary alloys, we show that frozen transfer learning with 10–20% of the data (hundreds of datapoints) achieves similar accuracies to models trained from scratch (on thousands of datapoints). Moreover, we show that an equally accurate, but significantly more efficient surrogate model can be built using the transfer learned potential as the ground truth. In combination, we present a simulation workflow for machine learning potentials that improves data efficiency and computational efficiency.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"677 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652617","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

5d orbital induced room temperature quantum anomalous Hall effect in TbCl TbCl中5d轨道诱导的室温量子反常霍尔效应

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-17 DOI: 10.1038/s41524-025-01732-0

Jianqi Zhong, Jianzhou Zhao, Jinyu Zou, Gang Xu

引用次数: 0

Cross-modality material embedding loss for transferring knowledge between heterogeneous material descriptors 跨模态材料嵌入损失在异质材料描述符之间传递知识

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-16 DOI: 10.1038/s41524-025-01723-1

Gyoung S. Na

{"title":"Cross-modality material embedding loss for transferring knowledge between heterogeneous material descriptors","authors":"Gyoung S. Na","doi":"10.1038/s41524-025-01723-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01723-1","url":null,"abstract":"Despite the remarkable successes of transfer learning in materials science, the practicality of existing transfer learning methods are still limited in real-world applications of materials science because they essentially assume the same material descriptors on source and target materials datasets. In other words, existing transfer learning methods cannot utilize the knowledge extracted from calculated crystal structures when analyzing experimental observations of real-world chemical experiments. We propose a transfer learning criterion, called cross-modality material embedding loss (CroMEL), to build a source feature extractor that can transfer knowledge extracted from calculated crystal structures to prediction models in target applications where only simple chemical compositions are accessible. The prediction models based on transfer learning with CroMEL showed state-of-the-art prediction accuracy on 14 experimental materials datasets in various chemical applications. In particular, the prediction models with CroMEL achieved R2-scores greater than 0.95 in predicting the experimentally measured formation enthalpies and band gaps of the experimentally synthesized materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645644","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

Refining coarse-grained molecular topologies: a Bayesian optimization approach 细化粗粒度分子拓扑：贝叶斯优化方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-16 DOI: 10.1038/s41524-025-01729-9

Pranoy Ray, Adam P. Generale, Nikhith Vankireddy, Yuichiro Asoma, Masataka Nakauchi, Haein Lee, Katsuhisa Yoshida, Yoshishige Okuno, Surya R. Kalidindi

{"title":"Refining coarse-grained molecular topologies: a Bayesian optimization approach","authors":"Pranoy Ray, Adam P. Generale, Nikhith Vankireddy, Yuichiro Asoma, Masataka Nakauchi, Haein Lee, Katsuhisa Yoshida, Yoshishige Okuno, Surya R. Kalidindi","doi":"10.1038/s41524-025-01729-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01729-9","url":null,"abstract":"Molecular Dynamics (MD) simulations are vital for predicting the physical and chemical properties of molecular systems across various ensembles. While All-Atom (AA) MD provides high accuracy, its computational cost has spurred the development of Coarse-Grained MD (CGMD), which simplifies molecular structures into representative beads to reduce expense but sacrifice precision. CGMD methods like Martini3, calibrated against experimental data, generalize well across molecular classes but often fail to meet the accuracy demands of domain-specific applications. This work introduces a Bayesian Optimization-based approach to refine Martini3 topologies—specifically the bonded interaction parameters within a given coarse-grained mapping—for specialized applications, ensuring accuracy and efficiency. The resulting optimized CG potential accommodates any degree of polymerization, offering accuracy comparable to AA simulations while retaining the computational speed of CGMD. By bridging the gap between efficiency and accuracy, this method advances multiscale molecular simulations, enabling cost-effective molecular discovery for diverse scientific and technological fields.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645646","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