A computational method combining generative AI with atomistic simulations can identify promising platinum alloy catalyst structures for hydrogen fuel cells, report researchers from Science Tokyo.
Personalized course recommendations represent a significant problem in extensive e-learning platforms, as student preferences are in constant flux and educational environments are increasingly ...
Electrochemical deposition, or electroplating, is a common industrial technique that coats materials to improve corrosion resistance and protection, durability and hardness, conductivity and more. A ...
DeSCOPE is a single-cell perturbation prediction framework designed for scRNA-seq, scATAC-seq, and general single-cell–level perturbation modeling. It is built on a conditional Variational Autoencoder ...
Additional visualizations highlighting the comparison between the proposed two-stage AG-VQ-VAE network (without skip connections) and the single-stage AG-UNet (with skip connections) are presented.
CatDRX is a generative AI framework developed at Institute of Science Tokyo, which enables the design of new chemical catalysts based on the specific chemical reactions in which they are used. The ...
Abstract: We present a variational autoencoder (VAE) learning framework with introspective training for conditional image synthesis, and explore conditional capsule encoder by class-wise mask label ...
Abstract: In this article, we propose a novel conditional generative flow-induced variational autoencoder (CGlow-VAE) model to address the critical challenge of the small sample issue in plasma ...
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