Despite their rapid emergence as the dominant paradigm for electrochemical energy
storage, the full promise of lithium-ion batteries is yet to be fully realized, partly because of …

The origins of performance degradation in batteries can be traced to atomistic phenomena,
accumulated at mesoscale dimensions, and compounded up to the level of electrode …

Finding quantitative descriptors representing the microstructural features of a given material
is an ongoing research area in the paradigm of Materials-by-Design. Historically …

The greatest challenge when using deep convolutional neural networks (DCNNs) for
automatic segmentation of microstructural X-ray computed tomography (XCT) data is the …

Data-driven thermal and percolation analyses are conducted to elucidate the effects of
various characteristics on the effective thermal conductivity of complex 3D composite …

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is
important for tailoring the functionality of individual nanocrystals and their ensemble …

In the last decade, two-dimension materials with reduced symmetry have attracted a lot of
attention due to the emerging quantum features induced by their structural asymmetry. Two …

We propose a novel iterative segmentation algorithm (iS Sy. Da. TA: Iterative Segmentation
Synthetic Data Training Algorithm) employing Deep Convolutional Neural Networks and …

This article introduces an innovative classification methodology for identifying nanowires
within scanning electron microscope images. Our approach employs advanced image …

This paper demonstrates the use of a neural network with additional training on synthetic
data to identify, quantify, and measure short carbon fibers in electron microscope …