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npj Computational Materials
Scopus
ISSN 2057-3960 · Nature Portfolio · 1.794 artikel di indeks
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Artikel Jurnal
Many-body perturbation theory vs. density functional theory: a systematic benchmark for band gaps of solids
Abstract We benchmark many-body perturbation theory against density functional theory (DFT) for the band gaps of solids. We systematically compare four G W variants—G 0 W 0 using the Godby-Needs plasmon-pole approximation (G 0 W 0-PPA), full-frequency quasiparticle G 0 W 0 (QPG 0 W 0)…
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Artikel Jurnal
Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data
Abstract High-entropy carbide ceramics (HECCs) possess promising properties for extreme high-temperature applications. Machine learning offers an effective pathway to accelerate the discovery of novel HECCs, but data imbalance poses challenges for predictive performance. Here, we integrate the…
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Impact of electronic correlations on the superconductivity of high-pressure CeH9
Abstract Rare-earth superhydrides have attracted considerable attention because of their high critical superconducting temperature under extreme pressures. They are known to have localized valence electrons, implying strong electronic correlations. However, such many-body effects are rarely…
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Reply to: An explanation for the Rule of Four in Inorganic Materials
Abstract The Rule of Four captures the observation that in experimentally known inorganic stoichiometric compounds the number of atoms in the primitive cell shows an unusual abundance of cases where that number is a multiple of four. The previous comment provides some insightful and appealing…
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Artikel Jurnal
A crystal graph convolutional neural network framework for predicting stacking fault energy in concentrated alloys
Abstract The stacking fault energy (SFE) in concentrated alloys is highly sensitive to the local atomic environment. Understanding this relationship necessitates extensive sampling of configurations, which significantly increases computational demands. To address this challenge, we propose a graph…
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Artikel Jurnal
A possible explanation for the Rule of Four in Inorganic Materials
Abstract Recently, Gazzarrini et al. observed a ‘rule of four’, where the number of atoms in a primitive unit cell for inorganic materials taken from large databases has been observed to favour multiples of four1. The number of atoms in a primitive cell is given by the product of the number of…
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Artikel Jurnal
A non-orthogonal representation for materials based on chemical similarity
Abstract We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-property relationships. Our proposed…
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A generative material transformer using Wyckoff representation
Abstract Materials play a critical role in various technological applications. Identifying and enumerating stable compounds—those near the convex hull—is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally…
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Artikel Jurnal
Accelerating electron diffraction analysis using graph neural networks and attention mechanisms
Abstract Electron diffraction(ED) often used to solve for unknown structures or refine existing ones. Existing methods for automated ED analysis often struggle with challenges such as computational expense and experimental noise. This study introduces a deep learning framework to accelerate and…
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Artikel Jurnal
Unraveling the role of Fe and Ni in oxygen evolution reaction on pentlandite using three generations of computational surface models
Abstract Oxygen evolution reaction (OER)—2H2O → O2 + 4H+ + 4e−—remains the primary bottleneck in electrochemical water splitting for green hydrogen production. Pentlandite, a bimetallic chalcogenide mineral, has recently shown promise under alkaline conditions, although the elementary processes at…
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Artikel Jurnal
Graph embedding tensor: unifying topological description, symmetry detection, and structure generation for two-dimensional carbon allotropes
Abstract We introduce the embedding tensor Φ, a rank-3, purely integer tensor that elevates faces and edges to the same footing as vertices to provide a unique, coordinate-free representation of any three-connected two-dimensional carbon lattice. Defined by the incidence of vertices, edges, and…
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Artikel Jurnal
Compositional complexity buffers free-volume sensitivity and serrated flow in metallic glasses
Abstract Processing history imprints metallic glasses (MGs), yet whether compositional complexity desensitizes structure and mechanics to quench rate remains unresolved. We use large-scale molecular dynamics along a controlled Cu-Zr complexity ladder, Cu50Zr50, Cu47.5Zr47.5Al5, and Cu45Zr45Al5Ti5…
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Artikel Jurnal
Importance of ligand on-site interactions for the description of Mott-insulators in DFT+DMFT
Abstract Calculations combining density functional theory (DFT) and dynamical mean-field theory (DMFT) for transition metal (TM) oxides and similar compounds usually focus on improving the description of the TM d states. Here, we emphasize the importance of also accounting for corrections of the…
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Artikel Jurnal
Micromagnetics of conical-helix textures in thin films with different kinds of Dzyaloshinskii-Moriya interactions
Abstract Chiral spin textures in ferromagnetic materials with Dzyaloshinskii-Moriya interactions (DMIs) have attracted significant interest in recent years owing to their potential applications in nanodevices. This work focuses on describing stable conical-helix configurations hosted in ultrathin…
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Artikel Jurnal
Integrating deep-learning-based magnetic model and non-collinear spin-constrained method: methodology, implementation and application
Abstract We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent…
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Artikel Jurnal
AI-assisted rapid crystal structure generation towards a target local environment
Abstract In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating…
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Artikel Jurnal
LAMBench: a benchmark for large atomistic models
Abstract Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles calculations of atomistic systems. However, our understanding of the extent to which these models…
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Artikel Jurnal
Hierarchical transfer learning: an agile and equitable strategy for machine-learning interatomic models
Abstract Machine-learned interatomic models are growing in popularity due to their ability to afford near quantum-accurate predictions for complex phenomena with orders-of-magnitude greater computational efficiency. However, these models struggle when applied to systems of many element types due to…
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