A novel hybrid architecture combining graph convolutional neural networks with group contribution methods to predict vapor pressures of organic molecules with state-of-the-art accuracy.
Integrating tgBoost into large-scale chemical kinetics software to resolve contrasting experimental and simulation results on aerosol viscosity.
A new prediction method powered by machine learning and molecular embeddings to predict glass transition temperature of organic compounds with unprecedented accuracy.
Quantifying environmentally persistent free radicals and ROS generation in highway fine particulate matter using electron paramagnetic resonance spectroscopy.
Explicit modeling of isoprene photooxidation and α-pinene ozonolysis using GECKO-A to simulate viscosity of secondary organic aerosols.
Implementing Runge-Kutta algorithms to solve Ordinary Differential Equations at scale to reproduce mass-independent isotopic anomalies in volcanic sulphates, and understand sulphur oxidation pathways in tropospheric plumes.