AI-driven ionic liquid discovery with unified chemical intelligence

arXiv:2511.11257 · cs.AI, cs.CE, cs.LG · Submitted 2025-11-14 · Read on arXiv

cs.AI, cs.CE, cs.LG

Submitted: 2025-11-14

Updated: 2026-09-26

Code: https://github.com/lm-sys/FastChat

Terminology

• Alignment — 12× in this paper · explained in 1 episode
• Accuracy — 11× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Graph Neural Networks — 4× in this paper
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Ab Initio — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Graph-Text Grounding — 2× in this paper
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feed-Forward Networks — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
• ChemLLM: A Chemical Large Language Model
• Translation between Molecules and Natural Language
• Learning Transferable Visual Models From Natural Language Supervision
• Toolformer: Language Models Can Teach Themselves to Use Tools
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
• MathArena: Evaluating LLMs on Uncontaminated Math Competitions
• MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
• ReAct: Synergizing Reasoning and Acting in Language Models
• Large Language Models are Zero-Shot Reasoners
• The Semantic Scholar Open Data Platform
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• CoCa: Contrastive Captioners are Image-Text Foundation Models
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
• MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
• Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
• LoRA: Low-Rank Adaptation of Large Language Models
• Improved Baselines with Visual Instruction Tuning
• Momentum Contrast for Unsupervised Visual Representation Learning

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