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Found 5 papers, 3 papers with code
Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models
We introduce Equivariant Latent Progressive Distillation, a fast sampling algorithm that preserves geometric equivariance and accelerates generation from latent diffusion models.
AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design
Here we introduce AdsorbRL, a Deep Reinforcement Learning agent aiming to identify potential catalysts given a multi-objective binding energy target, trained using offline learning on the Open Catalyst 2020 and Materials Project data sets.
Multi-Objective Reinforcement Learning
reinforcement-learning
ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?
Evaluating the accuracy of outputs generated by Large Language Models (LLMs) is especially important in the climate science and policy domain.
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning
Deep learning in computational biochemistry has traditionally focused on molecular graphs neural representations; however, recent advances in language models highlight how much scientific knowledge is encoded in text.
Improving extreme weather events detection with light-weight neural networks
To advance automated detection of extreme weather events, which are increasing in frequency and intensity with climate change, we explore modifications to a novel light-weight Context Guided convolutional neural network architecture trained for semantic segmentation of tropical cyclones and atmospheric rivers in climate data.
