Unraveling Complex Reacting Systems in Energy and the Environment via Multiscale Data-Driven Approaches

Understanding and being able to predict the outcomes of complex reaction systems would be invaluable to varied scientific and engineering disciplines, ranging from enabling future energy, aerospace, and chemical processing technologies to understanding the Earth’s climate and the potential origins of life beyond Earth. Yet, unraveling and making predictions of complex reaction systems, which often proceed via ~100s of intermediate chemical species and ~1000s of intermediate reactions, are notoriously challenging tasks. A common theme is that physics and data from a single scale are rarely sufficient to enable definitive explanations and truly predictive models. Here, I describe how combining ab initio theoretical chemistry, multiscale modeling, and data science aids in overcoming such challenges. These ideas will be demonstrated primarily via examples relevant to energy and the environment, including combustion and emissions formation for carbon-free fuels (e.g., as long-duration energy storage media). Extensions of these ideas to other domains (e.g., atmospheric fate of compounds emitted from trees and oceans on Earth, atmospheres of other planets, plasmas, and chemical processing) will also be explored. I will close with the vision that linking automated versions of the methods discussed – which is not entirely unfeasible – would effectively create a “robotic scientific community” of experimentalists, theoreticians, and modelers that collaborate to advance scientific understanding of complex reacting systems at an accelerated pace.