Fully Automatized Optimization of Ring-Opening Reactions in Lactone Derivatives via Two-Step Machine Learning

Cyclization and cycloreversion of organic compounds are fundamental kinetic processes in the design of functional molecules, molecular machines, nanoscale sensors, and switches in the field of molecular and nanoelectronics. We present a fully automatic computational platform for the design of a class of five- and six-membered ring lactones by optimizing the ring-opening reaction rate. Starting from a minimal initial parent set, our algorithm generates iteratively cascades of pools of candidate lactone derivatives where optimization and down-selection are performed without human supervision. We employ the density functional theory combined with the transition state theory to elucidate the exact mechanism leading to the lactone ring-opening reaction. On the basis of the analysis of the reaction pathway and the frontier molecular orbitals, we identify a simple descriptor that can easily correlate with the reaction rate. Consequently, we can omit computationally expensive transition state calculations and deduce the reaction rate from simple ground-state and ionic calculations. To accelerate the platform, we use a data set of the order of 800 molecules to train machine learning models for the prediction of targeted chemical properties, reducing the computational time by a 90% factor. We developed an evolutionary algorithm capable of generating data sets 3 orders of magnitude larger than the initial parent set. Thus, we can explore a large domain of chemical space using minimal computational effort. Our entire platform is modular, and our current implementation for lactone can be further generalized to more complex systems via substitution of the quantum chemical and fingerprinting modules.