MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery
DOI:
https://doi.org/10.1609/aaai.v38i21.30565Keywords:
Artificial Intelligence, Software and testing tools for developing AI technologies, Simulation environments for AI agents and multi-agent systemsAbstract
AI-driven materials discovery is evolving rapidly with new approaches and pipelines for experimentation and design. However, the pipelines are often designed in isolation. We introduce a modular reinforcement learning framework for inter-operable experimentation and design of tailored, novel molecular species. The framework unifies reinforcement learning (RL) pipelines and allows the mixing and matching of choices for the underlying chemical action space, molecular representation, desired molecular properties, and RL algorithm. Our demo showcases the framework's capabilities applied to benchmark problems like quantitative estimate of drug-likeness and PLogP, as well as the design of novel small molecule solvents for carbon capture.Downloads
Published
2024-03-24
How to Cite
Fare, C., Holt, G. K., Chiazor, L., Smyrnakis, M., Tracey, R., & Hoang, L. (2024). MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23787-23789. https://doi.org/10.1609/aaai.v38i21.30565
Issue
Section
AAAI Demonstration Track