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Found 8 papers, 5 papers with code
ContextMix: A context-aware data augmentation method for industrial visual inspection systems
With the minimal additional computation cost of image resizing, ContextMix enhances performance compared to existing augmentation techniques.
Bespoke Nanoparticle Synthesis and Chemical Knowledge Discovery Via Autonomous Experimentations
The optimization of nanomaterial synthesis using numerous synthetic variables is considered to be extremely laborious task because the conventional combinatorial explorations are prohibitively expensive.
Robustness of SAM: Segment Anything Under Corruptions and Beyond
Following by interpreting the effects of synthetic corruption as style changes, we proceed to conduct a comprehensive evaluation for its robustness against 15 types of common corruption.
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples
Beyond the basic goal of mask removal, we further investigate and find that it is possible to generate any desired mask by the adversarial attack.
Machine vision for vial positioning detection toward the safe automation of material synthesis
Although robot-based automation in chemistry laboratories can accelerate the material development process, surveillance-free environments may lead to dangerous accidents primarily due to machine control errors.
Predicting failure characteristics of structural materials via deep learning based on nondestructive void topology
The combined PH and DML processing of 3D X-CT data is our unique approach enabling reliable failure predictions at the time of material examination based on void topology progressions, and the method can be extended to various nondestructive failure tests for practical use.
Identification of Crystal Symmetry from Noisy Diffraction Patterns by A Shape Analysis and Deep Learning
On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e. g., up to 230 space groups), severely limiting its practical usage.
Slab Graph Convolutional Neural Network for Discovery of N2 Electroreduction Catalysts
The catalyst development for N2 electroreduction reaction (NRR) with low onset potential and high Faradaic efficiency is highly desired, but remains challenging.
Materials Science
