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4 results for “amorphous silicon”
Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations"
<p>This is the data set for the preprint <a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a> [cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> 1). NEMD<br> 2). NEPpotential<br> 3). PDOS<br> 4). kappa-quenchRate<br> 5). kappa-size<br> 6). kappa-temperature<br> <br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.</p> <p> </p>
Research data for "Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning"
<p>This dataset contains structural data and ML local energies for the publication "Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning". Details of the contents can be found in README.txt. Code to analyse structures and reproduce figures from the publication are available at https://github.com/MorrowChem/understanding_defects</p>
Advanced Materials - Epitaxial Growth of Large-Scale 2D CrTe2 Films on Amorphous Silicon Wafers With Low Thermal Budget
<p>2D van der Waals (vdW) magnets open landmark horizons in the development of innovative spintronic device architectures. However, their fabrication with large scale poses challenges due to high synthesis temperatures (>500 °C) and difficulties in integrating them with standard complementary metal-oxide semiconductor (CMOS) technology on amorphous substrates such as silicon oxide (SiO<sub>2</sub>) and silicon nitride (SiN<em><sub>x</sub></em>). Here, a seeded growth technique for crystallizing CrTe<sub>2</sub> films on amorphous SiN<em><sub>x</sub></em>/Si and SiO<sub>2</sub>/Si substrates with a low thermal budget is presented. This fabrication process optimizes large-scale, granular atomic layers on amorphous substrates, yielding a substantial coercivity of 11.5 kilo-oersted, attributed to weak intergranular exchange coupling. Field-driven Néel-type stripe domain dynamics explain the amplified coercivity. Moreover, the granular CrTe<sub>2</sub> devices on Si wafers display significantly enhanced magnetoresistance, more than doubling that of single-crystalline counterparts. Current-assisted magnetization switching, enabled by a substantial spin–orbit torque with a large spin Hall angle (85) and spin Hall conductivity (1.02 ×  10<sup>7</sup> ℏ/2e  Ω⁻¹  m⁻¹), is also demonstrated. These observations underscore the proficiency in manipulating crystallinity within integrated 2D magnetic films on Si wafers, paving the way for large-scale batch manufacturing of practical magnetoelectronic and spintronic devices, heralding a new era of technological innovation.</p>
High-Quality Amorphous Silicon Carbide for Hybrid Photonic Integration Deposited at a Low Temperature - Supportive Information
<p>Integrated photonic platforms have proliferated in recent years, each demonstrating its own unique strengths and shortcomings. However, given the processing incompatibilities of different platforms, a formidable challenge in the field of integrated photonics still remains for combining the strength of different optical materials in one hybrid integrated platform. Silicon carbide is a material of great interest because of its high refractive index, strong second and third-order non-linearities and broad transparecy window in the visible and near infrared. However, integrating SiC has been difficult, and current approaches rely on transfer bonding techniques, that are time consuming, expensive and lacking precision in layer thickness. Here, we demonstrate high index Amorphous Silicon Carbide (a-SiC) films deposited at 150∘C and verify the high performance of the platform by fabricating standard photonic waveguides and ring resonators. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.