npj Computational Materials最新文献_第2页

Nanoscale confinement of phonon flow and heat transport 声子流动和热输运的纳米尺度约束

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-07 DOI: 10.1038/s41524-025-01593-7

Albert Beardo, Weinan Chen, Brendan McBennett, Tara Karimzadeh Sabet, Emma E. Nelson, Theodore H. Culman, Henry C. Kapteyn, Joshua L. Knobloch, Margaret M. Murnane, Ismaila Dabo

引用次数: 0

Automated generation of structure datasets for machine learning potentials and alloys 机器学习电位和合金结构数据集的自动生成

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-07 DOI: 10.1038/s41524-025-01669-4

Marvin Poul, Liam Huber, Jörg Neugebauer

{"title":"Automated generation of structure datasets for machine learning potentials and alloys","authors":"Marvin Poul, Liam Huber, Jörg Neugebauer","doi":"10.1038/s41524-025-01669-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01669-4","url":null,"abstract":"We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials (MLIP) for multicomponent alloys, called Automated Small SYmmetric Structure Training or ASSYST. Based on exploring the full space of random crystal structures with space groups, it facilitates the construction of training sets for MLIPs in an automatic way without prior knowledge of the material in question. The advantages of this approach are that only cells consisting of few atoms (≈ 10) are needed for the DFT training set, and the size and completeness of the data set can be systematically controlled with very few parameters. We validate that potentials fitted this way can accurately describe a wide range of binary and ternary phases, random alloys, as well as point and extended defects, that have not been part of the training set. Finally, we estimate the binary phase diagrams with good experimental agreement. We demonstrate that the overall excellent performance is not a coincidence, but a consequence of the extensive sampling in phase space of ASSYST. Overall, this means that ASSYST will enable the largely autonomous generation of high-quality DFT reference data and MLIPs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237563","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

Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling 通过机器学习驱动的原子模型揭示过饱和非晶氧化铝中氢的化学状态

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-06 DOI: 10.1038/s41524-025-01676-5

Simon Gramatte, Olivier Politano, Noel Jakse, Claudia Cancellieri, Ivo Utke, Lars P. H. Jeurgens, Vladyslav Turlo

{"title":"Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling","authors":"Simon Gramatte, Olivier Politano, Noel Jakse, Claudia Cancellieri, Ivo Utke, Lars P. H. Jeurgens, Vladyslav Turlo","doi":"10.1038/s41524-025-01676-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01676-5","url":null,"abstract":"Advancing hydrogen-based technologies requires detailed characterization of hydrogen chemical states in amorphous materials. As experimental probing of hydrogen is challenging, interpretation in amorphous systems demands accurate structural models. Guided by experiments on atomic layer deposited alumina, a fast atomistic simulation technique is introduced using an ab initio-based machine learning interatomic potential to generate amorphous structures with realistic hydrogen contents. As such, the annealing of highly defective crystalline hydroxide structures at atomic layer deposition temperatures reproduces experimental density and structure, enabling accurate prediction of Al Auger parameter chemical shifts. Our analysis shows that higher hydrogen content favors OH ligands, whereas lower hydrogen content leads to diverse chemical states and hydrogen bonding, consistent with charge density and partial Bader charge calculations. Our approach offers a robust route to link hydrogen content with experimentally accessible chemical shifts, aiding the design of next-generation hydrogen-related materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"102 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237128","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

Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints 基于机器学习的高强度锌合金多约束优化设计策略

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-06 DOI: 10.1038/s41524-025-01666-7

Chenfeng Pan, Wenwen Lin, Jianxing Zhou, Wei Jian, Ka Chun Chan, Yuk Lun Chan, Lu Ren

{"title":"Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints","authors":"Chenfeng Pan, Wenwen Lin, Jianxing Zhou, Wei Jian, Ka Chun Chan, Yuk Lun Chan, Lu Ren","doi":"10.1038/s41524-025-01666-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01666-7","url":null,"abstract":"Zinc (Zn) alloys offer advantages such as abundant resources and low cost. Nevertheless, their current mechanical properties limit application in more advanced fields. Due to the lack of clear compositional design methods, the development of high-performance Zn alloys is urgently needed. To this end, this work proposes a fast and effective design strategy for Zn alloys based on machine learning (ML). The prediction models for the ultimate tensile strength, elongation, and hardness were successfully developed, with accuracies exceeding 90%. Interpretability analysis of the models was performed using the SHAP method with particle swarm optimization (PSO). Furthermore, a ML-based Zn alloy composition design system (ZACDS) was proposed by integrating the Bayesian optimization algorithm. A novel high-strength Zn alloy was successfully designed using ZACDS, demonstrating good agreement between predicted and experimental mechanical properties. This approach offers a new strategy for Zn alloy design under different compositional constraints and performance requirements.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228928","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

Importance of enforcing Hund’s rules in density functional theory calculations of rare earth magnetocrystalline anisotropy 在稀土磁晶各向异性密度泛函理论计算中执行洪德规则的重要性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-05 DOI: 10.1038/s41524-025-01632-3

Y. Lee, Z. Ning, R. Flint, R. J. McQueeney, I. I. Mazin, Liqin Ke

引用次数: 0

An unsupervised machine learning based approach to identify efficient spin-orbit torque materials 基于无监督机器学习的高效自旋轨道转矩材料识别方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-03 DOI: 10.1038/s41524-025-01626-1

Shehrin Sayed, Hannah Calzi Kleidermacher, Giulianna Hashemi-Asasi, Cheng-Hsiang Hsu, Sayeef Salahuddin

{"title":"An unsupervised machine learning based approach to identify efficient spin-orbit torque materials","authors":"Shehrin Sayed, Hannah Calzi Kleidermacher, Giulianna Hashemi-Asasi, Cheng-Hsiang Hsu, Sayeef Salahuddin","doi":"10.1038/s41524-025-01626-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01626-1","url":null,"abstract":"Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"26 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211374","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 long-range quadrupole effects in 2D MSi2N4: impacts on electric and thermal transport 二维MSi2N4中增强的远程四极效应：对电和热输运的影响

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-03 DOI: 10.1038/s41524-025-01672-9

Juan Zhang, Jiayi Gong, Hongyu Chen, Lei Peng, Hezhu Shao, Yan Cen, Jun Zhuang, Heyuan Zhu, Jinjian Zhou, Hao Zhang

{"title":"Enhanced long-range quadrupole effects in 2D MSi2N4: impacts on electric and thermal transport","authors":"Juan Zhang, Jiayi Gong, Hongyu Chen, Lei Peng, Hezhu Shao, Yan Cen, Jun Zhuang, Heyuan Zhu, Jinjian Zhou, Hao Zhang","doi":"10.1038/s41524-025-01672-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01672-9","url":null,"abstract":"Long-range higher-order multipolar electron–phonon (e-ph) interactions beyond the dipole-like Fröhlich interactions have long been neglected in the description of various physical properties. Here we demonstrate the contribution from quadrupole effect to the electric and thermal transport properties of monolayer MSi2N4 (M = Mo/W) systems. The quadrupole effect reduces the electron and hole mobilities at 300 K by 25.4%, 12.8% for MoSi2N4, and by 19.2%, 52.3% for WSi2N4, respectively. For n- and p-type monolayers with modest dopings by fixing the carrier concentration to 1.0 × 1014 cm−2, the dipole-like e-ph interaction decreases the three-phonon-limited lattice thermal conductivities κl by 17.9% and 43.5% for monolayer MoSi2N4 and WSi2N4, respectively. However, further considerations of quadrupole e-ph interaction shrink such reductions of three-phonon-limited κl to only 3.6% and 2.4%, respectively due to the cancellation effects. Our results highlight the potential of MSi2N4 monolayers as promising candidates for advanced micro-electronic applications.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"36 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144202165","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

Discovery of chemically modified higher tungsten boride by means of hybrid GNN/DFT approach 用杂化GNN/DFT方法发现化学修饰的高硼化钨

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-02 DOI: 10.1038/s41524-025-01628-z

Nikita A. Matsokin, Roman A. Eremin, Anastasia A. Kuznetsova, Innokentiy S. Humonen, Aliaksei V. Krautsou, Vladimir D. Lazarev, Yuliya Z. Vassilyeva, Alexander Ya. Pak, Semen A. Budennyy, Alexander G. Kvashnin, Andrei A. Osiptsov

{"title":"Discovery of chemically modified higher tungsten boride by means of hybrid GNN/DFT approach","authors":"Nikita A. Matsokin, Roman A. Eremin, Anastasia A. Kuznetsova, Innokentiy S. Humonen, Aliaksei V. Krautsou, Vladimir D. Lazarev, Yuliya Z. Vassilyeva, Alexander Ya. Pak, Semen A. Budennyy, Alexander G. Kvashnin, Andrei A. Osiptsov","doi":"10.1038/s41524-025-01628-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01628-z","url":null,"abstract":"High-throughput search for new crystal structures is extensively assisted by data-driven solutions. Here we address their prospects for more narrowly focused applications in a data-efficient manner. To verify and experimentally validate the proposed approach, we consider the structure of higher tungsten borides, WB4.2, and eight metals as W substituents to set a search space comprising 375k+ inequivalent crystal structures for solid solutions. Their thermodynamic properties are predicted with errors of a few meV/atom using graph neural networks fine-tuned on the DFT-derived properties of ca. 200 entries. Among the substituents considered, Ta provides the widest range of predicted stable concentrations and leads to the most considerable changes in mechanical properties. The vacuumless arc plasma method is used to perform synthesis of higher tungsten borides with different concentrations of Ta. Vickers hardness of WB5-x samples with different Ta contents is measured, showing increase in hardness.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144193130","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

Quantitative prediction of optical static refractive index in complex oxides 复合氧化物中光学静态折射率的定量预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-31 DOI: 10.1038/s41524-025-01648-9

Lan Yang, Xiao Zhou, Xudong Ni, Li Huang, Lianduan Zeng, Zhongyang Wang, Jun Song, Tongxiang Fan

引用次数: 0

Parameter efficient multi-model vision assistant for polymer solvation behaviour inference 聚合物溶剂化行为推理的参数高效多模型视觉辅助

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-31 DOI: 10.1038/s41524-025-01658-7

Zheng Jie Liew, Ziad Elkhaiary, Alexei A. Lapkin

{"title":"Parameter efficient multi-model vision assistant for polymer solvation behaviour inference","authors":"Zheng Jie Liew, Ziad Elkhaiary, Alexei A. Lapkin","doi":"10.1038/s41524-025-01658-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01658-7","url":null,"abstract":"Polymer–solvent systems exhibit complex solvation behaviours encompassing a diverse range of phenomena, including swelling, gelation, and dispersion. Accurate interpretation is often hindered by subjectivity, particularly in manual rapid screening assessments. While computer vision models hold significant promise to replace the reliance on human evaluation for inference, their adoption is limited by the lack of domain-specific datasets tailored, in our case, to polymer–solvent systems. To bridge this gap, we conducted extensive screenings of polymers with diverse physical and chemical properties across various solvents, capturing solvation characteristics through images, videos, and image–text captions. This dataset informed the development of a multi-model vision assistant, integrating computer vision and vision-language approaches to autonomously detect, infer, and contextualise polymer–solvent interactions. The system combines a 2D-CNN module for static solvation state classification, a hybrid 2D/3D-CNN module to capture temporal dynamics, and a BLIP-2-based contextualisation module to generate descriptive captions for solvation behaviours, including vial orientation, solvent discolouration, and polymer interaction states. Computationally efficient, this vision assistant provides an accurate, objective, and scalable solution in interpreting solvation behaviours, fit for autonomous platforms and high-throughput workflows in material discovery and analysis.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188880","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