Integrated Study on Methane Activation: Exploring Main Group Frustrated Lewis Pairs through Density Functional Theory, Machine Learning, and Machine-Learned Force Fields

Frustrated Lewis Pairs (FLP) are an important advance in metal-free catalysis due to their ability to activate a variety of small molecules. Many studies have focused on a very limited sample of Lewis acids and bases. Herein, we disclose an automated exploration algorithm using density functional methods, artificial neural networks (ANNs), and a molecule builder that incentivizes the exploration of favorable FLP space for the activation of methane via two mechanisms: deprotonation and hydride abstraction. The exploration algorithm creates FLPs with different Lewis acids (LA), Lewis bases (LB), and their substituents (L-A/L-B), which proved successful in quickly converging in the favorable chemical space, suggesting chemically sound structures, and generating thousands of potential candidates for methane activating FLPs. By modeling thousands of reactions, an FLP database of methane activation was created, allowing one to data mine properties, e.g., adduct bond length, highest occupied molecular orbital-lowest-unoccupied molecular orbital (HOMO-LUMO) gap, global electrophilicity index, favored Lewis acids/bases/substituents, and substituent steric volume. These properties not only successfully narrow the FLP chemical space but also provide meaningful insight into the chemical nature of competent methane activators. The machine learning discovery strategy disclosed here is general enough to be applicable to many chemical optimization tasks. This study also investigates the efficacy of a Machine-Learned Force Field (MLFF) in predicting the formation energies of Frustrated Lewis Pairs (FLPs). Our model, exhibiting a test error of +/- 10 kcal/mol, highlighted impressive computational efficiency by enabling the calculation of all possible FLP permutations within our chemical space. The MLFF demonstrated proficiency in predicting energies, providing a significant acceleration compared to quantum mechanics methods. However, challenges emerged in accurately capturing forces, necessitating recourse to classical force fields for reliable structure relaxation. The present study sheds light on the MLFF's potential as a tool for rapid energy predictions, emphasizing the need for further refinement to enhance its accuracy, particularly in force predictions, to expand its utility in chemical simulations.