Publications

📜 Gas adsorption meets deep geometric learning: points, set and match

Developing AIdsorb, a deep learning framework that takes as input a molecular point cloud and predicts gas adsorption properties.

📜 Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks

Developing RetNet, a 3D convolutional neural network that takes as input a 3D energy image and predicts gas adsorption properties.

📜 Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage

Comparing ML approaches regarding their efficiency for identifying high-performing MOFs regarding hydrogen storage.

📜 Comparison of Energy-Based Machine Learning Descriptors for Gas Adsorption

Comparing enery-based descriptors regarding their impact on the performance of ML models for predicting uptake of various gases in MOFs.