Autonomous AI for Chemistry

Scientific discovery is increasingly moving toward autonomous research systems that integrate artificial intelligence with automated experimentation and computation. In our group, we explore the development of autonomous AI systems capable of guiding the discovery and optimization of molecules and materials with minimal human intervention.

Our research focuses on building AI agents and decision-making frameworks that can plan experiments, evaluate results, and iteratively improve candidate materials. These systems integrate machine learning models, optimization algorithms, and scientific reasoning to create intelligent discovery pipelines. By connecting AI models with computational simulations and experimental platforms, we aim to enable closed-loop materials discovery systems that accelerate research cycles and significantly reduce the time required to discover new functional materials.

Selected papers

[1]  Y. Kang and J. Kim*

ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models

Nature Communications,  2024

[2]  J. Bai#, A. Aldossary#, T. Swanick, M. Müller, Y. Kang, J. Zhang, J. W. Lee, T. W. Ko, M. G. Vakili, V. Bernales*, A. Aspuru-Guzik*

El Agente Gráfico: Structured Execution Graphs for Scientific Agents

Arxiv,  2026x

[3]  C. Choi#, Y. Zou#, M. Müller, H. Hao, Y. Kang, J. B. Pérez-Sánchez, I. Gustin, H. Xu, M. G. Vakili, C. Crebolder, A. Aspuru-Guzik*, V. Bernales*

El Agente Estructural: An Artificially Intelligent Molecular Editor

Arxiv,  2026