Understanding the chemical stability of active pharmaceutical molecules can affect the quality, safety, robustness, and efficacy of a drug product. The ultimate goal of this project is to develop a predictive tool for drug resistance to oxidation and degradation to facilitate early developmental efforts of potential pharmaceuticals, even before they are synthesized. We use detailed chemical kinetic models automatically generated with on-the-fly quantum chemical thermo-kinetic computations.
Design of an AI tool able to attain a fuel mixture composition that possesses specific desirable combustion characteristics. Given a constrained chemical search space and a target function (e.g., ignition delay time), this tool will predict which fuel composition is optimal for the task. This tool will intelligently design a wide range of fuel systems efficiently, attaining experimentally-validated results, while at the same time reducing the amount of required experiments and resources.
This project focuses on attaining robust proton-exchange membranes (PEMs) for high-temperature fuel cells. The key challenge with today’s PEMs at high temperature oxidative environments is that they degrade too quickly, resulting in an unacceptably low operation time of the overall device. We generate detailed chemical kinetic models for the degradation of these materials, and use neural networks to predict new polymer structures with low degradation rates at these extreme operation conditions.
