npj Computational Materialsnpj计算材料
npj Computational Materials(英文缩写 NPJ COMPUT MATER),ISSN 2057-3960,eISSN 2057-3960,中文译名:npj计算材料 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-28 至 2026-09-28，按本站收录文献的发表日期统计。
期刊介绍
npj Computational Materials 最新收录文献
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1. Uncertainty-aware machine learning for core-loss background subtraction in EELS.
PMID:日期:2026-01-01Quantitative electron energy-loss spectroscopy (EELS) critically depends on accurate background subtraction, yet standard approaches rely on deterministic parametric fits and provide no statistically rigorous uncertainty estimates. This limitation is particularly severe in the core-loss regime, where the background must be extrapolated into the ionisation-edge window, hindering reliable and automated analyses. Here, we introduce a physics-informed, uncertainty-aware machine learning framework that reformulates core-loss background subtraction as a statistically controlled inference problem with explicit uncertainty propagation. The method combines Monte Carlo replica generation based on empirically determined covariance matrices with neural network ensembles trained on the pre-edge region, and enforces physically consistent extrapolation via an effective-exponent constraint at the edge onset. This approach yields pixel- and energy-resolved background probability distributions, enabling direct propagation of uncertainty to derived spectroscopic observables. Closure tests on synthetic spectral images demonstrate faithful background reconstruction and well-calibrated uncertainty estimates, including in extrapolation regions. Applied to core-loss EELS spectral images of twisted CrSBr, the framework reveals nanoscale, stacking-correlated modulations of Cr L white-line intensities that significantly exceed propagated uncertainties. More broadly, this work provides an automation-ready pathway for quantitative, uncertainty-aware core-loss EELS analysis across diverse materials systems.
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2. PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.
PMID:日期:2026-01-01Self-driving laboratories accelerate materials discovery by autonomously designing and executing experiments through closed-loop integration of robotics and artificial intelligence. Active learning with Gaussian processes has enabled efficient phase diagram mapping, reducing required measurements by approximately 80% compared to conventional grid sampling. However, current approaches treat each system independently, discarding accumulated knowledge despite systematic similarities across related materials families. Here we introduce PhaseTransfer, a transfer learning framework that leverages previously characterized phase diagrams to accelerate mapping of new systems. PhaseTransfer combines a target model trained on current data with source models from related diagrams using spatially varying reliability assessments and focusing sampling on regions where transferred knowledge proves unreliable. Validation across synthetic benchmarks and experimental implementation on an autonomous microfluidic platform for biological condensate screening demonstrates an order of 50% reduction in sampling requirements compared to conventional active learning. By enabling knowledge reuse across investigations, transfer learning substantially enhances both the efficiency and generality of autonomous materials discovery.
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3. Grain-level micromechanical modeling and assessment of fatigue-critical pores using graph neural networks.
PMID:日期:2026-01-01Fatigue assessment is essential for the insertion of new materials and manufacturing processes into engineering applications, yet experimental testing is costly and time-consuming, and while crystal plasticity (CP) captures microstructure-sensitive fatigue mechanisms, it remains computationally expensive. This work presents GFF-MAP (Grain-level Fatigue Failure - Micromechanical Assessment of Pores), a framework that leverages graph neural network (GNN) surrogate models trained on CP data to predict grain-level micromechanical fields in additively manufactured (AM) IN718 microstructures containing representative pore defects. The models predict grain-average and grain-maximum stress and accumulated plastic strain energy density, , a fatigue damage indicator linked to crack initiation. Although prediction accuracy decreases for extreme and plasticity-driven quantities, the predicted grain-maximum enables rapid assessment of pore criticality by determining whether fatigue hotspots occur in pore-adjacent grains. This approach advances GNN-based surrogate modeling of polycrystalline response by extending predictions to plasticity-driven localized fatigue metrics and enabling fast screening of fatigue-critical pores in AM components.
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4. Accurate and efficient protocols for high-throughput first-principles materials simulations.
PMID:日期:2026-01-01Advancements in theoretical and algorithmic approaches, workflow engines, and an ever-increasing computational power have enabled a novel paradigm for materials discovery through first-principles high-throughput simulations. A major challenge in these efforts is to automate the selection of parameters used by simulation codes to deliver numerical precision and computational efficiency. Here, we propose a rigorous methodology to assess the quality of self-consistent DFT calculations with respect to smearing and -point sampling across a wide range of crystalline materials. For this goal, we develop criteria to reliably estimate average errors on total energies, forces, and other properties as a function of the desired computational efficiency, while consistently controlling -point sampling errors. The present results provide automated protocols (named standard solid-state protocols or SSSPr) for selecting optimized parameters based on different choices of precision and efficiency tradeoffs. These are available through open-source tools that range from interactive input generators for DFT codes to high-throughput workflows.
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5. GEMDAT: a Python toolkit for site-resolved diffusion analysis in solid-state molecular dynamics.
PMID:日期:2026-01-01Molecular dynamics (MD) simulations have become essential for understanding diffusion mechanisms in solid-state materials such as ionic conductors, fuel cells, and gas sensors, yet most existing studies and software tools extract only standard metrics, leaving much of the information contained in the trajectories unused. Here we introduce GEMDAT, a user-friendly Python toolkit for site-resolved diffusion analysis of MD simulations of solid-state materials (https://github.com/GEMDAT-repos/GEMDAT). Beyond mean-squared displacements, radial distribution functions, and Arrhenius-based activation energies, GEMDAT provides jump rates, attempt frequencies, site-specific activation energies, rotational diffusion, and percolation. Our tool provides access to vibrational amplitudes, site geometries, and site occupancies-quantities that are also directly comparable to experimental diffraction data. Migration sites can be defined manually or identified automatically from the trajectory. A built-in caching approach, together with rapid visualization capabilities, makes the workflow fast and interactive. We demonstrate GEMDAT on a series of case studies involving crystalline Li- and Na-ion conductors, plastic crystals, amorphous structures, and surface configurations, showing how the code extracts atomic-level structural features and connects them to macroscopic transport properties, thereby guiding the optimization and development of solid-state materials.
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6. {"_":"Automated modeling of polarons: defects and reactivity on TiO(110) surfaces.","sub":["2"]}
PMID:日期:2026-01-01Polarons are widespread in functional materials and are key to device performance in several technological applications. However, their effective impact on material behavior remains elusive, as condensed matter studies struggle to capture their intricate interplay with atomic defects in the crystal. In this work, we present an automated workflow for modeling polarons within density functional theory (DFT). Our approach enables a fully automatic identification of the most favorable polaronic configurations in the system. Machine learning techniques accelerate predictions, allowing for an efficient exploration of the defect-polaron configuration space. We apply this methodology to Nb-doped TiO(110) surfaces, providing new insights into the role of defects in surface reactivity. Using CO adsorbates as a probe, we find that Nb doping has minimal impact on reactivity, whereas oxygen vacancies contribute significantly depending on their local arrangement via the stabilization of polarons on the surface atomic layer. Our package streamlines the modeling of charge trapping and polaron localization with high efficiency, enabling systematic, large-scale investigations of polaronic effects across complex material systems.
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7. {"_":"Exploring charge density waves in two-dimensional NbSe with machine learning.","sub":["2"]}
PMID:日期:2026-01-01Niobium diselenide (NbSe) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CDWs-capturing effects such as layer number, twist angle, and strain-remains challenging due to the high computational cost of first-principles methods. Here, we develop a physically informed workflow for training machine-learning interatomic potentials (MLIPs) based on the E(3)-equivariant Allegro architecture, tailored to capture the subtle structural and dynamical signatures of CDWs in mono- and bilayer NbSe. We find that while CDW lattice distortions are relatively easy to learn, modeling vibrational properties remains more challenging. It requires targeted dataset design and careful hyperparameter tuning, pushing the boundaries and testing the extensibility of current MLIP frameworks. Our MLIPs enable reliable simulations of commensurate and incommensurate CDW phases, including their sensitivity to dimensionality and stacking, as well as CDW dynamics, phonons, and transition temperatures estimated via the stochastic self-consistent harmonic approximation. This work opens new possibilities for studying and tuning CDWs in NbSe and other two-dimensional systems, with implications for electron-phonon coupling, superconductivity, and advanced materials design.
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8. Morphology prediction of small nanoparticles in any orientation from single electron micrographs.
PMID:日期:2026-01-01Accurate and automated data analysis for transmission electron microscopy will enable new high-throughput experiments that can reveal atomic-scale structure-property relationships for many functional materials. A key challenge in this pursuit is scalable three-dimensional structure prediction from single two-dimensional images. Existing tomographic and atom-counting approaches require either high electron doses, complex acquisition schemes, or the object in specific orientations, limiting experimental design. Here, we introduce a diffusion-based generative workflow that predicts the three-dimensional morphology of nanoscale objects directly from a single scanning/transmission electron micrograph. Applied to sub-5 nm platinum nanoparticles on ceria, it successfully predicts reasonable structures across diverse particle morphologies and imaging orientations. Combined with automated data acquisition in experiments, we believe techniques like this could be an essential part in relating ensemble-level structural variation and dynamics with performance, particularly fitting for heterogeneous catalysis.
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9. Flexible uncertainty calibration for machine-learned interatomic potentials.
PMID:日期:2026-01-01Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) is a statistical framework that constructs prediction intervals with guaranteed coverage under minimal assumptions, making it an attractive tool for UQ. However, existing CP techniques, while offering formal coverage guarantees, often lack accuracy, scalability, and adaptability to the complexity of atomic environments. In this work, we present a flexible uncertainty calibration framework for MLIPs, inspired by CP but reformulated as a parameterized optimization problem. This formulation enables the direct learning of environment-dependent quantile functions, producing sharper and more adaptive predictive intervals at negligible computational cost. Using the foundation model MACE-MP-0 as a representative case, we demonstrate the framework across diverse benchmarks, including ionic crystals, catalytic surfaces, and molecular systems. Our results achieve substantial improvements in uncertainty-error correlation, improve the detection of high-error configurations for active learning, and transfer reliably across distinct exchange-correlation functionals. Importantly, it is general, data efficient, and compatible with diverse MLIP architectures and baseline UQ schemes, offering a practical route toward robust and transferable atomistic simulations.
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10. Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges.
PMID:日期:2026-01-01Recent advancements in vision language models (VLMs) have opened new avenues for analyzing complex visual data. Models such as ChatGPT, Gemini, Llama and LLaVA have gained prominence for their ability to process both visual and textual data, excelling in tasks like natural image captioning, visual question answering (VQA), and reasoning. Similarly, the Segment Anything Model (SAM) by Meta has demonstrated remarkable segmentation capabilities. Given the importance of microscopy images in fields like biology, medicine, and materials science-where visual data is often analyzed alongside textual information from captions, reports, or literature-it is critical to evaluate the effectiveness of these models on such data. This study assesses the capabilities of ChatGPT-5, Gemini-2.5Pro, Llama-3.2V, LLaVA-1.5 and SAM-2 on classification, segmentation, counting, and VQA tasks using microscopy images. ChatGPT and Gemini excelled in comprehending microscopy images, while SAM performed well in object isolation. Although their performance falls short of domain expert accuracy, particularly when faced with complexities such as impurities, overlaps, and irrelevant artifacts, these models show clear gains compared to prior versions. These findings highlight the promise of VLMs in scientific image analysis and the need for further advancements to meet the demands of expert-level tasks.