npj Computational Materials最新文献_第2页

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

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

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

Range-separated hybrid functionals in full-potential LAPW using adaptively compressed exchange 使用自适应压缩交换的全电位LAPW中的距离分离混合函数

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-08 DOI: 10.1038/s41524-025-01733-z

Jānis Užulis, Aleksandr V. Sorokin, Andris Gulans

引用次数: 0

RAFFLE: active learning accelerated interface structure prediction RAFFLE：主动学习加速界面结构预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-08 DOI: 10.1038/s41524-025-01749-5

Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies, Steven Paul Hepplestone

{"title":"RAFFLE: active learning accelerated interface structure prediction","authors":"Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies, Steven Paul Hepplestone","doi":"10.1038/s41524-025-01749-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01749-5","url":null,"abstract":"Interfaces between materials are critical to the performance of many devices, yet predicting their structure is computationally demanding due to the vast configuration space. We introduce RAFFLE, a software package for efficiently exploring low-energy interface configurations between arbitrary crystal pairs, enabling the generation of ensembles of interface structures. RAFFLE leverages physical insights and genetic algorithms to intelligently sample configurations, using dynamically evolving 2-, 3-, and 4-body distribution functions as generalised structural descriptors. These descriptors are refined through active learning to guide atom placement strategies. RAFFLE performs well across diverse systems, including bulk materials, intercalation compounds, and interfaces. It correctly recovers known bulk phases of aluminum and MoS2, and predicts stable phases in intercalation and grain-boundary systems. For Si∣Ge interfaces, it finds intermixed and abrupt structures to be similarly stable. By accelerating interface structure prediction, RAFFLE offers a powerful tool for materials discovery, enabling efficient exploration of complex configuration spaces.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"15 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144797312","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

Advancing simulations of coupled electron and phonon nonequilibrium dynamics using adaptive and multirate time integration 利用自适应和多速率时间积分技术推进电子和声子耦合非平衡动力学模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-07 DOI: 10.1038/s41524-025-01738-8

Jia Yao, Ivan Maliyov, David J. Gardner, Carol S. Woodward, Marco Bernardi

{"title":"Advancing simulations of coupled electron and phonon nonequilibrium dynamics using adaptive and multirate time integration","authors":"Jia Yao, Ivan Maliyov, David J. Gardner, Carol S. Woodward, Marco Bernardi","doi":"10.1038/s41524-025-01738-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01738-8","url":null,"abstract":"Electronic structure calculations in the time domain provide a deeper understanding of nonequilibrium dynamics in materials. The real-time Boltzmann equation (rt-BTE), used in conjunction with accurate interactions computed from first principles, has enabled reliable predictions of coupled electron and lattice dynamics. However, the timescales and system sizes accessible with this approach are still limited, with two main challenges being the different timescales of electron and phonon interactions and the cost of computing collision integrals. As a result, only a few examples of these calculations exist, mainly for two-dimensional (2D) materials. Here we leverage adaptive and multirate time integration methods to achieve a major step forward in solving the coupled rt-BTEs for electrons and phonons. Relative to conventional (non-adaptive) time-stepping, our approach achieves a 10x speedup for a target accuracy, or greater accuracy by 3–6 orders of magnitude for the same computational cost, enabling efficient calculations in both 2D and bulk materials. This efficiency is showcased by computing the coupled electron and lattice dynamics in graphene up to ~100 ps, as well as modeling ultrafast lattice dynamics and thermal diffuse scattering maps in bulk materials (silicon and gallium arsenide). In addition to improved efficiency, our adaptive method can resolve the characteristic rates of different physical processes, thus naturally bridging different timescales. This enables simulations of longer timescales and provides a framework for modeling multiscale dynamics of coupled degrees of freedom in matter. Our work opens new opportunities for quantitative studies of nonequilibrium physics in materials, including driven lattice dynamics with phonons coupled to electrons, spin, and other degrees of freedom.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792480","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

An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations 基于交错物理的深度学习框架作为微结构小疲劳裂纹扩展模拟的新循环跳跃方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-05 DOI: 10.1038/s41524-025-01741-z

Vignesh Babu Rao, Ashley D. Spear

{"title":"An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations","authors":"Vignesh Babu Rao, Ashley D. Spear","doi":"10.1038/s41524-025-01741-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01741-z","url":null,"abstract":"Conventional fracture mechanics asserts that the relevant physics governing small crack growth occurs near the crack front. However, for fatigue, computing these physics for each crack-growth increment over the entire microstructurally small crack regime is computationally intractable. Properly trained deep-learning surrogate models can massively accelerate fatigue crack-growth predictions by virtually propagating an initial crack using micromechanical fields corresponding to just the initially cracked microstructure. As the predicted crack front advances, however, the fields no longer reflect relevant near-crack-front physics, leading to error and uncertainty accumulation. To address this, we present an interleaved physics-based deep-learning (PBDL) framework, where updates to the crack representation in the physics-based model are triggered intermittently using model uncertainty, thereby updating micromechanical fields passed to the deep-learning model. We show that this framework, representing a novel cycle-jumping approach, effectively limits error accumulation in history-dependent fatigue crack evolution and forms a template for other time-series applications in materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"126 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144787325","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