npj Computational Materials最新文献

{"title":"Amorphization evolution study of CrCoFeNiMn high entropy alloy for mechanical performance optimization by deep potential molecular dynamics","authors":"Wentao Zhou, Jia Song, Lve Lin, Huilong Yang, Shaoqiang Guo, Guang Ran, Yafei Wang","doi":"10.1038/s41524-025-01561-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01561-1","url":null,"abstract":"<p>In the study, we explore the structural evolution of Cantor high-entropy alloy (HEA) under different super-cooling rates and its correlation with mechanical property variations by the developed machine learning-driven deep potential molecular dynamics (DPMD) simulation. Our results reveal the critical super-cooling rate of amorphization-crystallization transition of Cantor alloy and the local structure constitutions at different temperatures during the super-cooling process. The associated mechanical property studies demonstrate the glassy Cantor alloy amorphized at high super-cooling rate exhibits a superior capability of ductility but this capability is unrelated to the amorphization cooling rates. While the high strength of Cantor alloy requires a lower super-cooling rate which might result in the crystallization, amorphizing the Cantor alloy at the critical super-cooling rate of amorphization-crystallization transition could compatibilize both ductility and strength capabilities. Such a discovery sheds new lights on the material development and its mechanical performance optimization for industrial applications.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"92 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608330","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}

{"title":"Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers","authors":"Sung Yun Lee, Do Hyung Cho, Chulho Jung, Daeho Sung, Daewoong Nam, Sangsoo Kim, Changyong Song","doi":"10.1038/s41524-025-01569-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01569-7","url":null,"abstract":"<p>Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies produce vast amounts of data that exceed meticulous human inspection capabilities. Despite the increasing demands, the full application of machine learning has been hindered by the need for data-specific optimizations. In this study, we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data. This method provides robust phase retrieval for simulated data and performs well on partially damaged and noisy single-pulse diffraction data from X-ray free-electron lasers. Moreover, the method significantly reduces data processing time, facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition. This approach offers a reliable solution to the phase problem to be widely adopted across various research areas confronting the inverse problem.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"53 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608326","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}

{"title":"New fast ion conductors discovered through the structural characteristic involving isolated anions","authors":"Qifan Yang, Jing Xu, Yuqi Wang, Xiao Fu, Ruijuan Xiao, Hong Li","doi":"10.1038/s41524-025-01559-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01559-9","url":null,"abstract":"<p>One of the key materials in solid-state lithium batteries is fast ion conductors. However, Li⁺ ion transport in inorganic crystals involves complex factors, making it a mystery to find and design ion conductors with low migration barriers. In this work, a distinctive structural characteristic involving isolated anions has been discovered to enhance high ionic conductivity in crystals. It is an effective way to create a smooth energy potential landscape and construct local pathways for lithium ion migration. By adjusting the spacing and arrangement of the isolated anions, these local pathways can connect with each other, leading to high ion conductivity. By designing different space groups and local environments of the Se²⁻ anions in the Li₈SiSe₆ composition, combined with the ion transport properties obtained from AIMD simulations, we define isolated anions and find that local environments with higher point group symmetry promotes the formation of cage-like local transport channels. Additionally, the appropriate distance between neighboring isolated anions can create coplanar connections between adjacent cage-like channels. Furthermore, different element types of isolated anions can be used to control the distribution of cage-like channels in the lattice. Based on the structural characteristic of isolated anions, we shortlisted compounds with isolated N3−, Cl⁻, I⁻, and S²⁻ features from the crystal structure databases. The confirmation of ion transport in these structures validates the proposed design method of using isolated anions as structural features for fast ion conductors and leads to the discovery of several new fast ion conductor materials.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"54 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608313","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}

{"title":"PID3Net: a deep learning approach for single-shot coherent X-ray diffraction imaging of dynamic phenomena","authors":"Tien-Sinh Vu, Minh-Quyet Ha, Adam Mukharil Bachtiar, Duc-Anh Dao, Truyen Tran, Hiori Kino, Shuntaro Takazawa, Nozomu Ishiguro, Yuhei Sasaki, Masaki Abe, Hideshi Uematsu, Naru Okawa, Kyosuke Ozaki, Kazuo Kobayashi, Yoshiaki Honjo, Haruki Nishino, Yasumasa Joti, Takaki Hatsui, Yukio Takahashi, Hieu-Chi Dam","doi":"10.1038/s41524-025-01549-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01549-x","url":null,"abstract":"<p>This paper introduces a deep learning (DL)-based method for phase retrieval tailored to single-shot, multiple-frame coherent X-ray diffraction imaging (CXDI), designed specifically for visualizing local nanostructural dynamics within a larger sample. Current phase retrieval methods often struggle with achieving high spatiotemporal resolutions, handling dynamic imaging, and managing computational costs, which limits their applicability in observing nanostructural dynamics. This study addresses these gaps by developing a novel method that leverages a feedforward architecture with a physics-informed strategy utilizing measurement settings, enabling the reconstruction of dynamic “movies\" from time-evolving diffraction images of the illuminated area. The method incorporates key enhancements, such as temporal convolution blocks to capture spatiotemporal correlations and a unified TV regularization applied to the reconstructed object, resulting in improved noise reduction and spatial smoothness. An expanded evaluation framework, including multiple metrics and systematic sensitivity analysis, is employed to comprehensively assess the method’s performance and robustness. Proof-of-concept experiments, including numerical simulations and imaging experiments of a moving Ta test chart and colloidal gold particles (dispersed in aqueous polyvinyl alcohol solutions) with synchrotron hard X-rays, validate the high imaging performance of this method. Experimental results demonstrate that structures in the sample have been successfully reconstructed at short exposure times, significantly outperforming both traditional methods and current DL-based methods. The proposed method provides efficient and reliable reconstruction of dynamic images with low computational costs, making it suitable for exploring fast-evolving phenomena in synchrotron- or free-electron laser-based applications requiring high spatiotemporal resolutions.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599874","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}

{"title":"The coupling of carbon non-stoichiometry and short-range order in governing mechanical properties of high-entropy ceramics","authors":"Wenyu Lu, Jingru Xu, Shasha Huang, Xuepeng Xiang, Haijun Fu, Xinlei Gu, Baichuan Xu, Ailin Yang, Zhenggang Wu, Shijun Zhao","doi":"10.1038/s41524-025-01551-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01551-3","url":null,"abstract":"<p>High-entropy carbide ceramics (HECCs) commonly exhibit non-stoichiometric compositions and short-range order (SRO) arising from diverse elemental mixing. In this study, taking (TiZrHfNb)C as a representative HECC, we explore the coupling effects of SRO and carbon non-stoichiometry based on density-functional theory (DFT) and machine learning (ML). DFT results indicate that carbon non-stoichiometry is favored in Ti and Nb environments due to enhanced local atomic relaxation and charge transfer, which contribute to improved <i>d-d</i> bonding interactions. DFT-based Monte Carlo (MC) simulations further reveal a clustering tendency of Ti and Nb elements that compete with carbon non-stoichiometry formation. These local features are effectively captured by ML models, enabling rapid assessment of the interplay among carbon deficiency, SRO, and their influences on the mechanical properties of HECCs. This work elucidates the microscopic local properties responsible for the macroscopic behavior, offering key insights for designing HECCs through careful element selection and local chemistry control.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"37 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570402","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}

{"title":"Data-efficient construction of high-fidelity graph deep learning interatomic potentials","authors":"Tsz Wai Ko, Shyue Ping Ong","doi":"10.1038/s41524-025-01550-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01550-4","url":null,"abstract":"<p>Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations. However, most MLPs today are trained on data computed using relatively cheap density functional theory (DFT) methods such as the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) functional. While meta-GGAs such as the strongly constrained and appropriately normed (SCAN) functional have been shown to yield significantly improved descriptions of atomic interactions for diversely bonded systems, their higher computational cost remains an impediment to their use in MLP development. In this work, we outline a data-efficient multi-fidelity approach to constructing Materials 3-body Graph Network (M3GNet) interatomic potentials that integrate different levels of theory within a single model. Using silicon and water as examples, we show that a multi-fidelity M3GNet model trained on a combined dataset of low-fidelity GGA calculations with 10% of high-fidelity SCAN calculations can achieve accuracies comparable to a single-fidelity M3GNet model trained on a dataset comprising 8 × the number of SCAN calculations. This work provides a pathway to the development of high-fidelity MLPs in a cost-effective manner by leveraging existing low-fidelity datasets.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"212 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575477","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}

{"title":"Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network","authors":"Luqi Dong, Xuanlin Zhang, Ziduo Yang, Lei Shen, Yunhao Lu","doi":"10.1038/s41524-025-01546-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01546-0","url":null,"abstract":"<p>The piezoelectric materials enable the mutual conversion between mechanical and electrical energy, which drive a multi-billion dollar industry through their applications as sensors, actuators, and energy harvesters. The third-rank piezoelectric tensor is the core matrices for piezoelectric materials and their devices. However, the high costs of obtaining full piezoelectric tensor data through either experimental or computational methods make a significant challenge. Here, we propose an equivariant attention tensor graph neural network (EATGNN) that can identify crystal symmetry and remain independent of the reference frame, ultimately enabling the accurate prediction of the complete third-rank piezoelectric tensor. Especially, we perform an irreducible decomposition of the piezoelectric tensor into four irreducible representations to efficiently reserve the symmetry under group transformation operations. Our results further demonstrate that this model performs well in both bulk and two-dimensional materials. Finally, combining EATGNN with first-principles calculations, we discovered several potential high-performance piezoelectric materials.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561222","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}

{"title":"A machine-learning framework for accelerating spin-lattice relaxation simulations","authors":"Valerio Briganti, Alessandro Lunghi","doi":"10.1038/s41524-025-01547-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01547-z","url":null,"abstract":"<p>Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence. Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progress is hindered by their high computational cost. Here we present an accelerated computational framework based on machine-learning models for the prediction of molecular vibrations and spin-phonon coupling coefficients. We apply this method to three open-shell coordination compounds exhibiting long relaxation times and show that this approach achieves semi-to-full quantitative agreement with ab initio methods reducing the computational cost by about 80%. Moreover, we show that this framework naturally extends to molecular dynamics simulations, paving the way to the study of spin relaxation in condensed matter beyond simple equilibrium harmonic thermal baths.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"46 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561220","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}

{"title":"Constructing multicomponent cluster expansions with machine-learning and chemical embedding","authors":"Yann L. Müller, Anirudh Raju Natarajan","doi":"10.1038/s41524-025-01543-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01543-3","url":null,"abstract":"<p>Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the <i>embedded cluster expansion</i> (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"67 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561219","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}

{"title":"Foundation models for materials discovery – current state and future directions","authors":"Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, Alessandro Curioni","doi":"10.1038/s41524-025-01538-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01538-0","url":null,"abstract":"<p>Large language models, commonly known as LLMs, are showing promise in tacking some of the most complex tasks in AI. In this perspective, we review the wider field of foundation models—of which LLMs are a component—and their application to the field of materials discovery. In addition to the current state of the art—including applications to property prediction, synthesis planning and molecular generation—we also take a look to the future, and posit how new methods of data capture, and indeed modalities of data, will influence the direction of this emerging field.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"212 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561221","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}