Active Optimization of Chemical Catalysts

Drastic changes in climate and global losses in biodiversity are increasing the need to shift the incumbent energy and chemical infrastructure from a fossil-fuel based system to a sustainable-energy based system. Such a system will require that the production of fuels and chemicals use only sustainable energy (e.g., solar) and simple, abundant feedstocks like carbon dioxide, water, or nitrogen.

In a Data Science Institute seminar on June 20, 2019, Kevin Tran joined electrochemistry and machine learning (ML) in a seminar titled “Active Optimization of Catalysts for Sustainable Energy and Chemistry.” Active optimization means iteratively using ML to decide which experiment to conduct. According to Tran, this approach can make a significant impact on the development of catalysts that could turn renewable electricity into sustainable fuels and chemicals.

Tran’s team at Carnegie Mellon University has developed a method for optimizing such chemistries. It combines an active optimization routine with a fully automated simulation framework—nicknamed GASpy—to screen the appropriate catalysts and reaction conditions. The seminar included an overview of the chemistry, simulation, and software aspects of this framework before detailing the team’s ML techniques, experimental designs, and statistical methods.

For example, the research team “tunes” variables (e.g., catalysts or voltages) and then uses density functional theory (DFT) to calculate the resulting effects on the performance of target chemistries. Thousands of calculations are needed, though, so Tran created a high-throughput, Python-based framework that automates these DFT calculations. Still, each calculation can take an hour or even days to run. GASpy speeds up this process by using ML models to automatically decide which calculations to perform next.

Tran noted, “Active optimization needs to balance exploitation of the model with exploration of the search space.” GASpy uses this balance to perform iterative DFT calculations and in recent tests found more than 100 high-performing catalyst surfaces. As planned, these results informed subsequent experiments: University of Toronto colleagues began experimenting with a number of these promising catalysts.

The research team aims to broaden GASpy’s capabilities with multi-objective and multi-fidelity optimization, which will make the framework more scalable and holistic. For instance, the roadmap includes optimizing catalyst efficiency and stability simultaneously while varying catalyst composition and other processing conditions. Tran’s team is also improving quantification and calibration of the model’s uncertainty. He added, “We’re looking at ways to judge how different active optimization methods compare to each other via retrospective and prospective performance metrics.”

Tran is pursuing a PhD in chemical engineering at Carnegie Mellon University, advised by Dr. Zachary Ulissi, and interning this summer at LLNL under the mentorship of Dr. Joel Varley. Tran was previously a fluoropolymer processing engineer at W. L. Gore & Associates, working on implantable medical devices. He received his bachelor’s in chemical engineering from the University of Delaware, where his research focused on microkinetic modeling for biopharmaceutical applications.