npj Computational Materials最新文献_第3页

Substrate-aware computational design of two-dimensional materials 二维材料的基板感知计算设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-21 DOI: 10.1038/s41524-025-01754-8

Arslan Mazitov, Ivan Kruglov, Alexey V. Yanilkin, Aleksey V. Arsenin, Valentyn S. Volkov, Dmitry G. Kvashnin, Artem R. Oganov, Kostya S. Novoselov

{"title":"Substrate-aware computational design of two-dimensional materials","authors":"Arslan Mazitov, Ivan Kruglov, Alexey V. Yanilkin, Aleksey V. Arsenin, Valentyn S. Volkov, Dmitry G. Kvashnin, Artem R. Oganov, Kostya S. Novoselov","doi":"10.1038/s41524-025-01754-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01754-8","url":null,"abstract":"Two-dimensional (2D) materials attract considerable attention due to their remarkable electronic, mechanical and optical properties. Despite their use in combination with substrates in practical applications, computational studies often neglect the effects of substrate interactions for simplicity. This study presents a novel method for predicting the atomic structure of 2D materials on substrates by combining an evolutionary algorithm, a lattice-matching technique, an automated machine-learning interatomic potentials training protocol, and the ab initio thermodynamics approach. Using the molybdenum-sulfur system on a sapphire substrate as a case study, we reveal several new stable and metastable structures, including previously known 1H-MoS2 and newly found Pmma Mo3S2, (Pbar{1}) Mo2S, P21m Mo5S3, and P4mm Mo4S, where the Mo4S structure is specifically stabilized by interaction with the substrate. Finally, we use the ab initio thermodynamics approach to predict the synthesis conditions of the discovered structures in the parameter space of the commonly used chemical vapor deposition technique.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"71 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901194","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

Capturing short-range order in high-entropy alloys with machine learning potentials 在具有机器学习潜力的高熵合金中捕获短时顺序

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-21 DOI: 10.1038/s41524-025-01722-2

Yifan Cao, Killian Sheriff, Rodrigo Freitas

引用次数: 0

Revealing nanostructures in high-entropy alloys via machine-learning accelerated scalable Monte Carlo simulation 通过机器学习加速可扩展蒙特卡罗模拟揭示高熵合金的纳米结构

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-20 DOI: 10.1038/s41524-025-01762-8

Xianglin Liu, Kai Yang, Yongxiang Liu, Fanli Zhou, Dengdong Fan, Zongrui Pei, Pengxiang Xu, Yonghong Tian

{"title":"Revealing nanostructures in high-entropy alloys via machine-learning accelerated scalable Monte Carlo simulation","authors":"Xianglin Liu, Kai Yang, Yongxiang Liu, Fanli Zhou, Dengdong Fan, Zongrui Pei, Pengxiang Xu, Yonghong Tian","doi":"10.1038/s41524-025-01762-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01762-8","url":null,"abstract":"First-principles Monte Carlo (MC) simulations at finite temperatures are computationally prohibitive for large systems due to the high cost of quantum calculations and poor parallelizability of sequential Markov chains in MC algorithms. We introduce scalable Monte Carlo at eXtreme (SMC-X), a generalized checkerboard algorithm designed to accelerate MC simulation with arbitrary short-range interactions, including machine learning potentials, on modern accelerator hardware. The GPU implementation, SMC-GPU, harnesses massive parallelism to enable billion-atom simulations when combined with machine-learning surrogates of density functional theory (DFT). We apply SMC-GPU to explore nanostructure evolution in two high-entropy alloys, FeCoNiAlTi and MoNbTaW, revealing diverse morphologies including nanoparticles, 3D-connected NPs, and disorder-stabilized phases. We quantify their size, composition, and morphology, and simulate an atom-probe tomography (APT) specimen for direct comparison with experiments. Our results highlight the potential of large-scale, data-driven MC simulations in exploring nanostructure evolution in complex materials, opening new avenues for computationally guided alloy design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"15 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901198","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

A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy 一个预训练的硫化物固体电解质深电位模型，覆盖范围广，精度高

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-20 DOI: 10.1038/s41524-025-01764-6

Ruoyu Wang, Mingyu Guo, Yuxiang Gao, Xiaoxu Wang, Yuzhi Zhang, Bin Deng, Mengchao Shi, Linfeng Zhang, Zhicheng Zhong

{"title":"A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy","authors":"Ruoyu Wang, Mingyu Guo, Yuxiang Gao, Xiaoxu Wang, Yuzhi Zhang, Bin Deng, Mengchao Shi, Linfeng Zhang, Zhicheng Zhong","doi":"10.1038/s41524-025-01764-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01764-6","url":null,"abstract":"Solid electrolytes with fast ion transport are crucial for solid state lithium metal batteries. Chemical doping has been the most effective strategy for improving ion condictiviy, and atomistic simulation with machine-learning potentials helps optimize doping by predicting ion conductivity for various composition. Yet most existing machine-learning models are trained on narrow chemistry, requiring retraining for each new system, which wastes transferable knowledge and incurs significant cost. Here, we propose a pre-trained deep potential model purpose-built for sulfide solid electrolytes with attention mechanism, known as DPA-SSE. The training set includes 15 elements and consists of both equilibrium and extensive out-of-equilibrium configurations. DPA-SSE achieves a high energy resolution of less than 2 meV/atom for dynamical trajectories up to 1150 K, and reproduces experimental ion conductivity with remarkable accuracy. DPA-SSE generalizes well to complex electrolytes with mixes of cation and anion atoms, and enables highly efficient dynamical simulation via model distillation. DPA-SSE also serves as a platform for continuous learning and can be fine-tuned with minimal downstream data. These results demonstrate the possibility of a new pathway for the AI-driven development of solid electrolytes with exceptional performance.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901197","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

SPRING, an effective and reliable framework for image reconstruction in single-particle Coherent Diffraction Imaging SPRING是一种有效、可靠的单粒子相干衍射成像图像重建框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-19 DOI: 10.1038/s41524-025-01661-y

Alessandro Colombo, Mario Sauppe, Andre Al Haddad, Kartik Ayyer, Morsal Babayan, Rebecca Boll, Ritika Dagar, Simon Dold, Thomas Fennel, Linos Hecht, Gregor Knopp, Katharina Kolatzki, Bruno Langbehn, Filipe R. N. C. Maia, Abhishek Mall, Parichita Mazumder, Tommaso Mazza, Yevheniy Ovcharenko, Ihsan Caner Polat, Dirk Raiser, Julian C. Schäfer-Zimmermann, Kirsten Schnorr, Marie Louise Schubert, Arezu Sehati, Jonas A. Sellberg, Björn Senfftleben, Zhou Shen, Zhibin Sun, Pamela H. W. Svensson, Paul Tümmler, Sergey Usenko, Carl Frederic Ussling, Onni Veteläinen, Simon Wächter, Noelle Walsh, Alex V. Weitnauer, Tong You, Maha Zuod, Michael Meyer, Christoph Bostedt, Davide E. Galli, Minna Patanen, Daniela Rupp

{"title":"SPRING, an effective and reliable framework for image reconstruction in single-particle Coherent Diffraction Imaging","authors":"Alessandro Colombo, Mario Sauppe, Andre Al Haddad, Kartik Ayyer, Morsal Babayan, Rebecca Boll, Ritika Dagar, Simon Dold, Thomas Fennel, Linos Hecht, Gregor Knopp, Katharina Kolatzki, Bruno Langbehn, Filipe R. N. C. Maia, Abhishek Mall, Parichita Mazumder, Tommaso Mazza, Yevheniy Ovcharenko, Ihsan Caner Polat, Dirk Raiser, Julian C. Schäfer-Zimmermann, Kirsten Schnorr, Marie Louise Schubert, Arezu Sehati, Jonas A. Sellberg, Björn Senfftleben, Zhou Shen, Zhibin Sun, Pamela H. W. Svensson, Paul Tümmler, Sergey Usenko, Carl Frederic Ussling, Onni Veteläinen, Simon Wächter, Noelle Walsh, Alex V. Weitnauer, Tong You, Maha Zuod, Michael Meyer, Christoph Bostedt, Davide E. Galli, Minna Patanen, Daniela Rupp","doi":"10.1038/s41524-025-01661-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01661-y","url":null,"abstract":"Coherent Diffraction Imaging (CDI) is an experimental technique to image isolated structures by recording the scattered light. The sample density can be recovered from the scattered field through a Fourier Transform operation. However, the phase of the field is lost during the measurement and has to be algorithmically retrieved. Here we present SPRING, an analysis framework tailored to X-ray Free Electron Laser (XFEL) single-shot single-particle diffraction data that implements the Memetic Phase Retrieval method to mitigate the shortcomings of conventional algorithms. We benchmark the approach on data acquired in two experimental campaigns at SwissFEL and European XFEL. Results reveal unprecedented stability and resilience of the algorithm’s behavior on the input parameters, and the capability of identifying the solution in conditions hardly treatable with conventional methods. A user-friendly implementation of SPRING is released as open-source software, aiming at being a reference tool for the CDI community at XFEL and synchrotron facilities.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"202 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901199","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

Uncertainty quantification for misspecified machine learned interatomic potentials 错误指定的机器学习原子间势的不确定度量化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-16 DOI: 10.1038/s41524-025-01758-4

Danny Perez, Aparna P. A. Subramanyam, Ivan Maliyov, Thomas D. Swinburne

{"title":"Uncertainty quantification for misspecified machine learned interatomic potentials","authors":"Danny Perez, Aparna P. A. Subramanyam, Ivan Maliyov, Thomas D. Swinburne","doi":"10.1038/s41524-025-01758-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01758-4","url":null,"abstract":"The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of settings, which has brought renewed interest in robust means to quantify uncertainties. In many practical settings where model complexity is constrained (e.g., due to performance considerations), misspecification — the inability of any one choice of model parameters to exactly match all training data — is a key contributor to errors that is often disregarded. Here, we employ a recent misspecification-aware regression technique to quantify parameter uncertainties, which is then propagated to a broad range of phase and defect properties in tungsten. The propagation is performed through both brute-force resampling and implicit Taylor expansion. The propagated misspecification uncertainties robustly quantify and bound errors on a broad range of material properties. We demonstrate application to recent foundational machine learning interatomic potentials, accurately predicting and bounding errors in MACE-MPA-0 energy predictions across the diverse materials project database.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901040","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

Prediction of intrinsic multiferroicity and large valley polarization in a layered Janus material 层状Janus材料的本征多铁性和大谷极化预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-16 DOI: 10.1038/s41524-025-01760-w

Yulin Feng, Shaoxuan Qi, Yangyang Ren, Meng Liu, Na Liu, Meifeng Liu, Qing Yang, Sheng Meng

{"title":"Prediction of intrinsic multiferroicity and large valley polarization in a layered Janus material","authors":"Yulin Feng, Shaoxuan Qi, Yangyang Ren, Meng Liu, Na Liu, Meifeng Liu, Qing Yang, Sheng Meng","doi":"10.1038/s41524-025-01760-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01760-w","url":null,"abstract":"Two-dimensional (2D) intrinsic multiferroics have attracted considerable attention for the next generation of advanced information technologies. Herein, we report that bilayer Janus FeSCl, a novel 2D system designed by substituting sulfur in monolayer 1T-FeCl2, exhibits a giant spontaneous valley polarization and intrinsic magnetoelectric coupling. This Janus structure exhibits a ground-state bilayer structure that breaks space-inversion symmetry, enabling sliding ferroelectricity. Each monolayer displays robust intralayer ferromagnetic ordering, while the bilayer hosts interlayer antiferromagnetic alignment with opposing magnetic moments. Crucially, ferrovalley-mediated coupling links ferroelectric polarization and antiferromagnetic order, allowing electric-field-driven magnetic reversal. Notably, the direction of the net magnetic moment can be reversed through ferroelectric polarization switching, enabling nonvolatile control of the magnetism. The elucidated mechanisms are generalizable to diverse 2D material families, offering a universal framework for designing atomic-scale multiferroics. This work not only establishes foundational insights into 2D multiferroics but also advances the understanding of coupled charge-spin-valley physics in low-dimensional systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901036","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

Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning 通过物理信息算子学习实现金属材料从图像分割到多尺度解决方案的数字化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-13 DOI: 10.1038/s41524-025-01718-y

Shahed Rezaei, Kianoosh Taghikhani, Alexandre Viardin, Reza Najian Asl, Ali Harandi, Nikhil Vijay Jagtap, David Bailly, Hannah Naber, Alexander Gramlich, Tim Brepols, Mustapha Abouridouane, Ulrich Krupp, Thomas Bergs, Markus Apel

{"title":"Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning","authors":"Shahed Rezaei, Kianoosh Taghikhani, Alexandre Viardin, Reza Najian Asl, Ali Harandi, Nikhil Vijay Jagtap, David Bailly, Hannah Naber, Alexander Gramlich, Tim Brepols, Mustapha Abouridouane, Ulrich Krupp, Thomas Bergs, Markus Apel","doi":"10.1038/s41524-025-01718-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01718-y","url":null,"abstract":"Fast prediction of microstructural responses based on realistic material topology is vital for linking process, structure, and properties. This work presents a digital framework for metallic materials using microscale features. We explore deep learning for two primary goals: (1) segmenting experimental images to extract microstructural topology, translated into spatial property distributions; and (2) learning mappings from digital microstructures to mechanical fields using physics-informed operator learning. Loss functions are formulated using discretized weak or strong forms, and boundary conditions-Dirichlet and periodic-are embedded in the network. Input space is reduced to focus on key features of 2D and 3D materials, and generalization to varying loads and input topologies are demonstrated. Compared to FEM and FFT solvers, our models yield errors under 1–5% for averaged quantities and are over 1000× faster during 3D inference.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"37 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144825812","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

Accelerating domain-aware electron microscopy analysis using deep learning models with synthetic data and image-wide confidence scoring 使用具有合成数据和图像范围置信度评分的深度学习模型加速领域感知电子显微镜分析

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-13 DOI: 10.1038/s41524-025-01756-6

M. J. Lynch, R. Jacobs, G. A. Bruno, P. Patki, D. Morgan, K. G. Field

引用次数: 0

Multi-target digital material design via a conditional denoising diffusion probability model 基于条件去噪扩散概率模型的多目标数字材料设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-11 DOI: 10.1038/s41524-025-01759-3

Wei Yue, Yuan Gao, Zhenliang Pan, Fanping Sui, Liwei Lin

引用次数: 0