EAGER: ADAPT: Optimizing Chemical Reaction Networks With AI

Todd Gingrich of Northwestern University is supported by an award from the by an award from the NSF Directorate of Mathematical and Physical Sciences, Artificial Intelligence Program, and the Division of Chemistry, to develop and assess AI algorithms for optimizing reaction-diffusion chemistry. As an example, the combination of reaction and diffusion enables living systems to regulate essential processes, e.g., how signals are processed and propagated in the brain.

Rather than functioning with electrons flowing through computer chips and wires, biological systems perform functions by molecular interconversion (reactions) and molecular diffusion through space. It is not, however, clear how to best mix the necessary reactions and diffusion to achieve a desired function. A significant barrier to designing reaction-diffusion chemistry is that the individual reactions and diffusion events occur with some randomness, and computational simulations must optimize the chemistry in the presence of noisy stochastic fluctuations.

Dr. Gingrich and his research group are pursuing computational approaches to mitigate the noise by developing new algorithms that utilize a mathematical construction called a tensor network, to effectively average over the noise. As another example, some chemical reactions can be generated that act as a clock with a molecule oscillating between low and high concentration.

The methods being developed are AI tools that would identify strategies to modulate the reaction-diffusion chemistry to regulate the oscillations. Those technical advances, built upon the iTensor software library, will be openly and freely disseminated. The research will be done in collaboration with the group of Tal Kachman, specializing in artificial intelligence (AI) at Radboud University (NL).

This project aims to develop AI algorithms that identify rate constants to optimize an objective function by gradient-based search, where the gradients measure improvements (e.g., in a chemical oscillator) due to small changes in the elementary reaction rates. The core technical challenge is to compute those gradients, a problem that demands accurate and efficient numerical solutions, for chemical kinetics in exceptionally high-dimensional space.

A naïve approach utilizes deterministic, coupled differential equations for the mass-action kinetics, but it is well-known that emergent chemical reaction network phenomena are only captured by algorithms that incorporate the stochastic nature of chemical kinetics. Algorithms like the Gillespie algorithm generate stochastic realizations of chemical kinetics and achieve accuracy by averaging over many noisy trajectories.

By utilizing the Doi-Peliti formalism’s analogies with quantum dynamics, Dr. Gingrich and his research group are developing and analyzing an alternative method that uses tensor networks to effectively average over all possible trajectories.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.