Hugging Face– tag –
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Hugging Face Reveals LLM Agent-Driven Hypothesis Generation for Materials Discovery, Accelerating Solid-State Exploration with MatExpert
Hugging Face USA Overview A recent collection of research papers published by Hugging Face includes advancements in hypothesis generation for materials discovery and design using goal-driven and constraint-guided LLM agents. Notably, the... -
ASPIRE Arrives on Hugging Face: Autonomous Skill Discovery System Streamlines Robot Programming
Hugging Face (arXiv paper page) USA Overview A preprint on Hugging Face introduces ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continuous learning system that autonomously develops and refines robot control ... -
Hugging Face Spotlights CrystalCLR and CHGNet for Enhanced Materials Property Prediction via Machine Learning
Hugging Face USA Overview Hugging Face highlights significant advancements in materials property prediction with the CrystalCLR framework and CHGNet machine learning interatomic potential. CrystalCLR improves material representations thr... -
Hugging Face Showcases LLM Application Preprints in Scientific Discovery, Emphasizing Autonomous Agents in Materials, Biology, and Chemistry
Hugging Face USA Overview Hugging Face's Daily Papers featured several arXiv preprints on the application of Large Language Models (LLMs) in scientific discovery. These papers focus on benchmarking LLMs across biology, chemistry, materia... -
Hugging Face Papers Unveil Electronic Density Generative Framework Combining 3D Convolutional Autoencoders and Latent Diffusion Models
Hugging Face International Overview A generative framework for learning electronic density's latent space dynamics has been introduced in Hugging Face's paper collection. This framework combines 3D convolutional autoencoders with latent ... -
MLIPs Tackle Electronic Entropy Challenge: Charge State Embedding Boosts Battery Material Prediction Accuracy
arXiv International Overview Traditional Machine Learning Interatomic Potentials (MLIPs) have struggled to capture electronic entropy in mixed-valence materials, leading to prediction inaccuracies. To address this, a new approach embeds ...
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