Machine learning for Chemistry

We develop machine learning methods to understand and design organic molecules and inoragnic materials. Our research focuses on learning structure–property relationships from large chemical and materials datasets using modern AI architectures such as graph neural networks, transformer-based models, and emerging foundation models for chemistry. These approaches enable the representation of complex molecular and materials structures and allow models to capture subtle chemical interactions that determine functional properties.

Beyond predictive modeling, we are particularly interested in generative models for inverse materials design. Techniques such as diffusion models, variational autoencoders, and autoregressive generative models enable the exploration of vast chemical design spaces and allow the creation of new molecules and materials with targeted properties. By combining generative AI with chemical knowledge and physical constraints, our goal is to develop intelligent design systems that can propose novel functional materials for applications in energy, catalysis, and advanced electronics.

Selected papers

[1]  Y. Kang#, H. Park#, B. Smit, and J. Kim*

A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks

Nature Machine Intelligence,  2023

[2]  H. Park#, Y. Kang#, and J. Kim*

Enhancing Structure–Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning

ACS Applied Materials & Interfaces,  2023

[3]  S. Han#, Y. Kang#, T. Bae, V. Bernales, A. Aspuru-Guzik*, J. Kim*

EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture

Arxiv,  2026