Why It's Important:  Chemical discovery is extremely challenging: the chemical space contains over 1060 possible molecules, yet only ~108 have been discovered. Traditional discovery cycles – hypothesis generation, virtual screening, synthesis, testing - take months or years per iteration and are expensive and largely manual. Current AI methods struggle with the multi-modal nature of chemical data (text, 2D graphs, 3D structures) and lack autonomous reasoning capabilities for multi-step planning and iterative refinement. While Large Language Models show promise for chemical tasks, they cannot effectively integrate diverse chemical modalities and underperform in complex tasks requiring synthesizability-aware design. A truly agentic, multimodal AI system could revolutionize molecular discovery by autonomously navigating chemical search spaces, generating testable hypotheses, and iteratively refining approaches based on feedback.

Chemical Flasks made of data plots surrounded by chemical element symbolsOur Approach: MM-ChemAgent aims to create a new foundational agentic multi-modal model for autonomous chemical discovery. Our approach combines four key innovations:

Enhanced Chemical Knowledge: Continued pre-training of open-source LLMs using a curated ~10B token corpus from chemical literature, patents, and scientific databases to embed deep domain expertise

Multi-Modal Understanding: Training the model to seamlessly integrate text descriptions with 2D molecular graphs and 3D atomic structures through unified representations, enabling cross-modal reasoning about structure-property relationships

Agentic Reasoning via RL: Reinforcement learning fine-tuning on multi-step chemical reasoning tasks to develop autonomous capabilities including hypothesis generation, tool use, experimental planning, and reflective self-correction

Evaluation & Impact: MM-ChemAgent will be evaluated on molecular optimization, retrosynthesis planning, and automated ML model design using established benchmarks plus applications in polymer design. All outputs will be open-sourced to benefit the research community. Once validated, this framework will guide autonomous lab development, ultimately enabling self-driving laboratories for scientific discovery and strengthening Georgia Tech’s leadership in AI for science.