Review of progress in calculation and simulation of high-temperature oxidation

Progress in Materials Science, Год журнала: 2024, Номер 147, С. 101348 - 101348

Опубликована: Июль 31, 2024

Язык: Английский

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Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Язык: Английский

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Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs

Journal of Applied Physics, Год журнала: 2025, Номер 137(1)

Опубликована: Янв. 2, 2025

First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using polycrystalline graphene as a case study, we demonstrate the feasibility simulating near first-principles accuracy containing over 1.4×106 atoms, achievable even consumer desktop GPUs. This is enabled by highly efficient neuroevolution potential (NEP) approach, implemented open-source GPUMD package. Leveraging NEP model’s and efficiency, quantify reduction thermal conductivity grain boundaries varying sizes, resolving contributions from in-plane out-of-plane (flexural) phonon modes. Additionally, find that can lead finite under significant tensile strain, contrast divergent behavior observed pristine similar conditions, indicating may play crucial role low-dimensional momentum-conserving systems. These findings could offer insights into interpreting experimental observations, given widespread presence both external strains real materials. The demonstrated ability simulate millions atoms near-first-principles on GPUs approach will help make high-fidelity atomistic more accessible broader research community.

Язык: Английский

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Goldene: An Anisotropic Metallic Monolayer with Remarkable Stability and Rigidity and Low Lattice Thermal Conductivity

Materials, Год журнала: 2024, Номер 17(11), С. 2653 - 2653

Опубликована: Май 31, 2024

In a recent breakthrough in the field of two-dimensional (2D) nanomaterials, first synthesis single-atom-thick gold lattice goldene has been reported through an innovative wet chemical removal Ti3C2 from layered Ti3AuC2. Inspired by this advancement, communication and for time, comprehensive first-principles investigation using combination density functional theory (DFT) machine learning interatomic potential (MLIP) calculations conducted to delve into stability, electronic, mechanical thermal properties single-layer free-standing goldene. The presented results confirm stability at 700 K as well remarkable dynamical stress-free strained monolayer. At ground state, elastic modulus tensile strength monolayer are predicted be over 226 12 GPa, respectively. Through validated MLIP-based molecular dynamics calculations, it is found that room temperature, nanosheet can exhibit anisotropic 9 GPa low conductivity around 10 ± 2 W/(m.K), We finally show native metallic nature stays intact under large strains. combined insights DFT provide understanding mechanical, electronic nanosheets.

Язык: Английский

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Chemical short-range order increases the phonon heat conductivity in a refractory high-entropy alloy

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 4, 2024

Язык: Английский

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Effects of thermal expansion and four-phonon interactions on the lattice thermal conductivity of the negative thermal expansion material ScF3

Physical review. B./Physical review. B, Год журнала: 2024, Номер 110(13)

Опубликована: Окт. 28, 2024

Язык: Английский

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Applications of machine learning in surfaces and interfaces

Chemical Physics Reviews, Год журнала: 2025, Номер 6(1)

Опубликована: Март 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Язык: Английский

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Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential

International Journal of Thermal Sciences, Год журнала: 2025, Номер 214, С. 109876 - 109876

Опубликована: Март 18, 2025

Язык: Английский

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Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics

Physical review. B./Physical review. B, Год журнала: 2025, Номер 111(9)

Опубликована: Март 18, 2025

Язык: Английский

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Advances in modeling complex materials: The rise of neuroevolution potentials

Chemical Physics Reviews, Год журнала: 2025, Номер 6(1)

Опубликована: Март 1, 2025

Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding physical and chemical properties materials. In recent years, machine-learned (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide desirable balance between accuracy computational cost. The neuroevolution potential (NEP) approach, implemented open-source GPUMD software, has emerged promising potential, exhibiting impressive exceptional efficiency. This review provides comprehensive discussion on methodological practical aspects NEP along with detailed comparison other representative state-of-the-art MLP approaches terms training accuracy, property prediction, We also demonstrate application approach to perform accurate efficient MD addressing complex challenges that traditional force fields typically cannot tackle. Key examples include structural liquid amorphous materials, order alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture two-dimensional nanoscale tribology, mechanical behavior compositionally alloys under various loadings. concludes summary perspectives future extensions further advance this rapidly evolving field.

Язык: Английский

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