Mario Boley

Mario Boley

Statistical Machine Learning, Optimisation, Applications to Materials Science and Chemistry

Projects

Graph Neural Networks for Guiding Chemical Synthesis of Metal–Organic Frameworks

Published:

This project will explore how graph neural networks (GNNs) can be used to probabilistically predict successful crystallization conditions in the reticular synthesis of novel metal–organic frameworks (MOFs). MOFs are rich chemical structures with important applications in carbon capture and storage, water harvesting from air, hydrogen storage, and catalysis—areas central to addressing global energy and sustainability challenges.

Human-Simulatable Additive Classification Models

Published:

Generalised additive classification models provide probabilistic predictions by adding the output of individual predictors to form a raw score $a=a_1+\dots+a_k$ and then by mapping this raw score to a probability $p(a)$. This process is considered human-interpretable because of the relative ease with which humans can carry out addition, and this is further enhanced by model fitting methods that restrict the individual predictor output to a small set of round values, i.e., $a_i \in \{-5, \dots, 5\}$. However, even when the resulting raw sum is round and easy to compute, the standard classification machinery based around the logistic transformation $p(a)=e^a/(1+e^a)$ renders it hard for humans to convert raw outputs to probabilities. For instance, a raw model output of $a=1$ corresponds to a probability $e/(1+e) \approx$ 0.7311 and $a=2$ corresponds to $e^2/(1+e^2)\approx 0.8808$.