Efficient Acceleration of Reaction Discovery in the Ab Initio Nanoreactor: Phenyl Radical Oxidation Chemistry.
The journal of physical chemistry. A
Over the years, many computational strategies have been employed to elucidate reaction networks. One of these methods is accelerated molecular dynamics, which can circumvent the expense required in dynamics to find all reactants and products (local minima) and transition states (first-order saddle points) on a potential energy surface (PES) by using fictitious forces that promote reaction events. The ab initio nanoreactor uses these accelerating forces to study large chemical reaction networks from first-principles quantum mechanics. In the initial nanoreactor studies, this acceleration was done through a piston periodic compression potential, which pushes molecules together to induce entropically unfavorable bimolecular reactions. However, the piston is not effective for discovering intramolecular and dissociative reactions, such as those integral to the decomposition channels of phenyl radical oxidation. In fact, the choice of accelerating forces dictates not only the rate of reaction discovery but also the types of reactions discovered; thus, it is critical to understand the biases and efficacies of these forces. In this study, we examine forces using metadynamics, attractive potentials, and local thermostats for accelerating reaction discovery. For each force, we construct a separate phenyl radical combustion reaction network using solely that force in discovery trajectories. We elucidate the enthalpic and entropic trends of each accelerating force and highlight their efficiency in reaction discovery. Comparing the nanoreactor-constructed reaction networks with literature renditions of the phenyl radical combustion PES shows that a combination of accelerating forces is best suited for reaction discovery.
View details for DOI 10.1021/acs.jpca.3c05484
View details for PubMedID 37934692
First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis.
2023; 14 (27): 7447-7464
Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.
View details for DOI 10.1039/d3sc01202f
View details for PubMedID 37449065
View details for PubMedCentralID PMC10337770
- First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis CHEMICAL SCIENCE 2023
The non-adiabatic nanoreactor: towards the automated discovery of photochemistry.
2021; 12 (21): 7294-7307
The ab initio nanoreactor has previously been introduced to automate reaction discovery for ground state chemistry. In this work, we present the nonadiabatic nanoreactor, an analogous framework for excited state reaction discovery. We automate the study of nonadiabatic decay mechanisms of molecules by probing the intersection seam between adiabatic electronic states with hyper-real metadynamics, sampling the branching plane for relevant conical intersections, and performing seam-constrained path searches. We illustrate the effectiveness of the nonadiabatic nanoreactor by applying it to benzene, a molecule with rich photochemistry and a wide array of photochemical products. Our study confirms the existence of several types of S0/S1 and S1/S2 conical intersections which mediate access to a variety of ground state stationary points. We elucidate the connections between conical intersection energy/topography and the resulting photoproduct distribution, which changes smoothly along seam space segments. The exploration is performed with minimal user input, and the protocol requires no previous knowledge of the photochemical behavior of a target molecule. We demonstrate that the nonadiabatic nanoreactor is a valuable tool for the automated exploration of photochemical reactions and their mechanisms.
View details for DOI 10.1039/d1sc00775k
View details for PubMedID 34163820
View details for PubMedCentralID PMC8171323
Hammett neural networks: prediction of frontier orbital energies of tungsten-benzylidyne photoredox complexes
2019; 10 (28): 6844–54
The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten-benzylidyne complexes of the form W([triple bond, length as m-dash]CArR)L4X. Using a reference molecular framework and the meta- and para-substituent Hammett parameters of the ligands, the models predict energies of frontier orbitals that correlate with redox potentials. The regressive models capture the multivariate character of electron-donating trends as influenced by multiple substituents even for non-aryl ligands, harnessing the breadth of Hammett parameters in a generalized model. We find a tungsten catalyst with tetramethylethylenediamine (tmeda) equatorial ligands and axial methoxyl substituents that should attract significant experimental interest since it is predicted to be highly reducing when photoactivated with visible light. The utilization of Hammett parameters in this study presents a generalizable and compact representation for exploring the effects of ligand substitutions.
View details for DOI 10.1039/c9sc02339a
View details for Web of Science ID 000476545100008
View details for PubMedID 31391907
View details for PubMedCentralID PMC6657405
Inverse Design of a Catalyst for Aqueous CO/CO2 Conversion Informed by the Ni-II-Iminothiolate Complex
2018; 57 (24): 15474–80
A computational inverse design method suitable to assist the development and optimization of molecular catalysts is introduced. Catalysts are obtained by continuous optimization of "alchemical" candidates in the vicinity of a reference catalyst with well-defined reaction intermediates and rate-limiting step. A NiII-iminoalkoxylate catalyst for aqueous CO/CO2 conversion is found with improved performance relative to a NiII-iminothiolate reference complex, previously reported as a biomimetic synthetic model of CO dehydroxygenase. Similar energies of other intermediates and transition states along the reaction mechanism show improved scaling relations relative to the reference catalyst. The linear combination of atomic potential tight-binding model Hamiltonian and the limited search of synthetically viable changes in the reference structure enable efficient minimization of the energy barrier for the rate-limiting step (i.e., formation of [LNiII(COOH)]-), bypassing the exponential scaling problem of high-throughput screening techniques. The reported findings demonstrate an inverse design method that could also be implemented with multiple descriptors, including reaction barriers and thermodynamic parameters for reversible reactivity.
View details for DOI 10.1021/acs.inorgchem.8b02799
View details for Web of Science ID 000453938700046
View details for PubMedID 30481007