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Computational Materials Today
Scopus
ISSN 2950-4635 · Elsevier · 60 artikel di indeks
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Artikel Jurnal
First-principles yet high-throughput screening of grafting-modified electronic structure in polymer insulator design
Polymer grafting is a strategic approach to modify its electrical performance for insulator applications. However, the graft effect on polymer electronic structure remains largely unexplored. Here, relying on first-principles simulation coupling classical molecular dynamics (MD), we introduce a…
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Artikel Jurnal
Deepcluster agent to search global minimum of nanoclusters using deep reinforcement learning: A review
Nanoclusters occupy an intermediate regime between molecules and bulk matter, where subtle changes in atomic arrangement can substantially modify optical, electronic, and catalytic behavior. Reliable identification of global minimum (GM) geometries therefore remains a central objective, yet the…
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Artikel Jurnal
Engineering strain-induced direct bandgap in high-order silicon polytypes
Silicon’s application and performance in optoelectronic devices are severely constrained by its inherent indirect bandgap (∼ 1.12 eV) and ineffective optical absorption. Here we predict a family of high-order hexagonal silicon polytypes (up to 24H) and show that modest biaxial tensile strain can…
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Artikel Jurnal
Machine-learning-guided discovery of direct-gap Si-Ge alloys in hexagonal and orthorhombic silicon allotropes
The pursuit of direct band gap silicon allotropes with enhanced photon absorption for photovoltaic applications has remained a longstanding challenge. While crystal structure prediction offers a theoretical route to discover such materials, it is often computationally expensive—even when aided by…
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Artikel Jurnal
Multiscale simulation of vacancy-induced defects in carbon nanotube nanocomposites: Mechanical implications
Carbon nanotubes (CNTs) are increasingly utilized as reinforcements in polymer composites for aerospace, mechanical, and automotive applications. However, a comprehensive understanding of the relationship between their nanostructure and resulting mechanical properties remains limited. In…
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Artikel Jurnal
NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentialsnep-data
Machine-learned coarse-grained (CG) models often suffer from noisy training data, limiting their accuracy and transferability. We propose a method to generate low-noise training data based on the potential of mean force by constraining CG beads during atomistic simulations and accumulating…
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Artikel Jurnal
Novel bora- and aza-triangulene-based graphyne and graphdiyne: Stable and stretchable narrow-gap semiconductors explored by first principles and machine learning
Following the recent experimental synthesis of the aza-triangulene kagome lattice (NTA-GYN), we present the design and a comprehensive theoretical investigation of a novel family of organic frameworks. Motivated by this breakthrough and the availability of bora-triangulene molecules, we propose the…
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Artikel Jurnal
Real-time steel core defect detection using enhanced lightweight object detection algorithm with data augmentation
Steel core defect detection encounters significant challenges, including limited sample data, inadequate real-time performance, and ambiguous defect characteristics, which severely restrict the engineering application of detection systems. To tackle these issues, this paper presents a real-time MSC…
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Artikel Jurnal
Improved data-driven modeling of sustainable high Tg polymers through topological feature analysis and enhanced property distribution
The reliable quantitative prediction of polymer glass transition temperature (Tg) is critical for designing polymeric materials with high thermal stability. In this study, we develop a machine learning framework that balances predictive accuracy and model interpretability for Tg prediction…
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Artikel Jurnal
A review of topological descriptors for amorphous materials complementing graph neural networks
Amorphous systems lack long-range order, making their properties strongly dependent on short-range order (SRO) and medium-range order (MRO) structural motifs and their network connectivity. While graph neural networks (GNNs) excel for crystalline materials, their performance on amorphous systems is…
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Artikel Jurnal
Machine learning driven exploration of hydride superconductors at ambient pressureAlexandria
Hydrogen-rich materials are among the most promising candidates for achieving high-temperature superconductivity due to their light atomic mass and strong phonon-mediated Cooper pairing. While many high-temperature superconducting hydrides have been reported experimentally, their practical…
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Artikel Jurnal
Machine learning drives a new paradigm in inorganic solid-state electrolytes research
The development of next-generation solid-state batteries (SSBs) relies heavily on the discovery of high-performance inorganic solid-state electrolytes (SSEs). However, conventional experimental and computational methods are often time-consuming and inefficient in exploring the vast compositional…
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Artikel Jurnal
Extreme quantum confinement and emergent phenomena in one-dimensional atomic wires
One-dimensional (1D) atomic wires represent the ultimate dimensional form of periodic materials, featuring distinctive structural characteristics in which their cross-sectional dimensions approach the atomic scale (∼1 nm), and they exhibit intrinsically self-passivated edges and pronounced…
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Artikel Jurnal
Design of deep-ultraviolet nonlinear optical BePNO2 guided by non-π-conjugated NLO-active unit
Deep ultraviolet (DUV) nonlinear optical (NLO) materials are urgently needed in modern laser technology, but the finding of a promising candidate is still of great challenge. It has been suggested that NLO-related properties of these materials are mainly determined by the functional units (FUs) in…
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Artikel Jurnal
Machine learning-based modeling of double perovskites via multi-output prediction of bandgap and dielectric properties
Double perovskites are promising candidates for next-generation photonics, optoelectronics, and photovoltaics owing to their excellent optical and electronic properties, low-cost fabrication, and reduced toxicity relative to lead-based perovskites. Systematic first-principles exploration is…
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Artikel Jurnal
Integrating physical descriptors into crystal graph convolution neural networks for designing 2D materials with large exciton binding energies
Exciton binding energy (Eb) is a critical parameter governing exciton stability, light–matter interaction, and electro-optical tunability in 2D semiconductors. However, accurately obtaining Eb typically involves high-cost methods, including experimental techniques such as optical absorption…
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Artikel Jurnal
Origin of metal-insulator transition in rare-earth Nickelates
Rare-earth nickelates RNiO3 (R=rare-earth element) undergo coupled structural, metal-insulator, and magnetic changes as temperature is lowered. Because the metal-insulator transition often occurs together with a symmetry-lowering distortion, it is commonly viewed as lattice driven. Here we use QSGW…
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Artikel Jurnal
Impact of 4f pseudopotential treatments on predicted high-Tc superconductivity in heavy rare-earth hexahydrides
Although heavy rare-earth hydrides have been widely predicted to host high-Tc superconductivity, the predicted pairing strength depends sensitively on the representation of 4f electrons within pseudopotential-based density functional perturbation theory (DFPT) calculations. We use cubic REH6 (RE =…
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Artikel Jurnal
Atomistic simulations of dielectric screening in large scale microelectronic devices at first principles accuracy
The dielectric screening of a microelectronic device and the related capacity-voltage (C-V) relationship is a major component in TCAD (technology computer-aided design) simulations. Traditionally the dielectric screening is described by a macroscopic continuum dielectric function in the Poisson…
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Artikel Jurnal
High-temperature superconducting electride in compressed lithium-silver compounds: Multicentered chemical bonding driven by interstitial spz-hybridization
The exploration of high-temperature superconductivity in lithium-rich compounds under extreme compression has revealed significant potential in quantum materials design. Here, we employ advanced crystal structure prediction and first-principles calculations to systematically investigate the Li-Ag…