Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery

arXiv:2609.02209 · physics.chem-ph, cs.LG · Submitted 2026-09-02 · Read on arXiv

physics.chem-ph, cs.LG

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/HongWxd/ProtoMI

Terminology

• Clustering — 37× in this paper · explained in 2 episodes
• Embeddings — 20× in this paper · explained in 2 episodes
• Contrastive Learning — 18× in this paper · explained in 5 episodes
• Adaptation — 15× in this paper · explained in 1 episode
• Cosine Similarity — 9× in this paper · explained in 3 episodes
• Representation Learning — 7× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Ablation — 3× in this paper · explained in 1 episode
• Loss Function — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Active Learning — 2× in this paper · explained in 6 episodes
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Small Molecule Accurate Recognition Technology — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Chemical Abstracts Service — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Graph Neural Network (GNN) — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes

Sources

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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