1 citations · 2 across the 5 of their papers we have counts for
5 papers
Deep learning-enabled large-scale analysis of particle geometry-lithiation correlations in battery cathode materials
Binbin Lin, Luis J. Carrillo, Xiang-Long Peng +4
A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical feat…
What can machine learning help with microstructure-informed materials modeling and design?
Xiang-Long Peng, Mozhdeh Fathidoost, Binbin Lin +2
Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of micr…
A deep learned nanowire segmentation model using synthetic data augmentation
Binbin Lin, Nima Emami, David A Santos +3
Automatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep-learning-based segmentation algorithms ha…
Mapping Humidity-dependent Mechanical Properties of a Single Cellulose Fibre
Julia Auernhammer, Tom Keil, Binbin Lin +4
Modelling of single cellulose fibres is usually performed by assuming homogenous properties, such as strength and Young s modulus, for the whole fibre. Additionally, the inhomogene…
Humidity Influence on Mechanics and Failure of Paper Materials: Joint Numerical and Experimental Study on Fiber and Fiber Network Scale
Binbin Lin, Julia Auernhammer, Jan-Lukas Schäfer +4
Paper materials are natural composite materials and well-known to be hydrophilic unless chemical and mechanical processing treatments are undertaken. The relative humidity impacts…