Density Functional Study of Methane Activation by Frustrated Lewis Pairs with Group 13 Trihalides and Group 15 Pentahalides and a Machine Learning Analysis of Their Barrier Heights.

Frustrated Lewis pairs (FLP) are an important advancement in metal-free catalysis. FLPs activate a variety of small molecules, notably dihydrogen. Methane activation, however, has not been reported despite it being an abundant chemical feedstock. Density functional theory calculations were utilized to elucidate the reaction mechanism of methane activation by triel trihalide and pnictogen pentahalide-ammonia Lewis pairs. Two reaction mechanisms were modeled for methane activation: proton abstraction and hydride abstraction. In all cases, deprotonation was thermodynamically and kinetically favored versus hydride abstraction. The use of heavier pnictogens and bigger triels were calculated to be more favorable for the activation of methane. To discern factors affecting the activation energies, different descriptors were correlated-ground state thermodynamics, orbital energies, transition state strain energies, etc.-but no consistent patterns were identified. Thus, machine learning methods were used to correlate ground state parameters to barrier heights. A neural network was used to correlate ground state descriptors (global electrophilicity index, bond dissociation energies, reaction energies) to activation free energies (R2 = 0.90).

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 (LA/LB), 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.