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Topological AI enables interpretable inverse design of catalytic active sites
A collaborative research team led by Professor Pan Feng from the School of New Materials at Peking University Shenzhen ...
AI Just Found the Future of Batteries, And It’s Not Lithium
AI has uncovered promising new materials that could make lithium-ion batteries obsolete and revolutionize energy storage.
Science Daily4d
AI just found 5 powerful materials that could replace lithium batteries
AI is helping scientists crack the code on next-gen batteries that could replace lithium-ion tech. By discovering novel ...
Tech Xplore on MSN2d
AI Discovers Five New Materials That Could Replace Lithium-Ion Batteries
A new AI tool has identified five promising metal oxide structures which could be used to replace lithium-ion batteries. The ...
Interesting Engineering on MSN6d
AI-driven breakthrough uncovers ‘next-gen’ materials that top lithium-ion performance
Specifically, the team used generative AI to discover new porous materials that could make multivalent-ion batteries a viable replacement for lithium-based systems. These next-generation batteries ...
Visual Studio Magazine4y
Generating Synthetic Data Using a Variational Autoencoder with PyTorch
A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...
ascopubs.org2mon
A transfer-learned hierarchical variational autoencoder model for ...
Methods: We developed a Deep Hierarchical Conditional Variational Autoencoder (CVAE) for de novo ACP design, using transfer learning by initializing the ESM-2 pre-trained encoder. A comprehensive ACP ...
EurekAlert!1y
Deep variational autoencoder for proteomics m | EurekAlert!
Next, Dear-DIA uses a variational autoencoder to extract the peak features of fragment ions and maps the features into Euclidean space, and then clusters the features, with different classes of ...