Comparative first principles-based molecular dynamics study of catalytic mechanism and reaction energetics of water oxidation reaction on 2D-surface

Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results

Quantum chemistry provides chemists with invaluable information, but the high computational cost limits the size and type of systems that can be studied. Machine learning (ML) has emerged as a means to dramatically lower the cost while maintaining high accuracy. However, ML models often sacrifice interpretability by using components such as the artificial neural networks of deep learning that function as black boxes. These components impart the flexibility needed to learn from large volumes of data but make it difficult to gain insight into the physical or chemical basis for the predictions. Here, we demonstrate that semiempirical quantum chemical (SEQC) models can learn from large volumes of data without sacrificing interpretability. The SEQC model is that of density-functional-based tight binding (DFTB) with fixed atomic orbital energies and interactions that are one-dimensional functions of the interatomic distance. This model is trained to ab initio data in a manner that is analogous to that used to train deep learning models. Using benchmarks that reflect the accuracy of the training data, we show that the resulting model maintains a physically reasonable functional form while achieving an accuracy, relative to coupled cluster energies with a complete basis set extrapolation (CCSD(T)*/CBS), that is comparable to that of density functional theory (DFT). This suggests that trained SEQC models can achieve a low computational cost and high accuracy without sacrificing interpretability. Use of a physically motivated model form also substantially reduces the amount of ab initio data needed to train the model compared to that required for deep learning models.

Site dependent catalytic water dissociation on an anisotropic buckled black phosphorus surface

Black phosphorus (BP) is unique among 2D materials due to its anisotropic puckered structure. It has been used as a multifunctional catalyst for various purposes. In this study, we performed first principles molecular dynamics simulations to understand the water-splitting reaction on a bi-layer BP surface. We focused on the site-specific aqueous reactivity of the buckled surface. A difference in the axis-dependent reactivity is observed owing to edge defects and exposed sites. Thus, we believe that BP edges, which significantly affect the interfacial water or organic solvent molecules, must exhibit very different edge-dependent reactivity. Experiments suggested the increasing catalytic efficiency of undisturbed BP in the order bulk, few-layered BP, and BP quantum dots. We choose three active sites to investigate the mechanistic details of the OER: the zigzag (ZZ), armchair (AC), and bulk sites. This study will provide insight into the enhanced catalytic activity when more edges are exposed as the active surface. We hope to clarify the reactive pathway in an aqueous solution supported by bi-layer BP by exploring the two different mechanisms for forming the OOH* complex. We explore and report two mechanisms: a simple push-pull reaction for oxygen-oxygen bond formation, the nucleophilic attack by formed OH^- and an attack by a water molecule. The free energy barriers procured for mechanism 1 taking place at the zigzag, armchair, and bulk sites are 7.59 ± 0.33, 9.04 ± 0.01, and 12.80 ± 0.09 kcal mol^-1, respectively. For mechanism 2 the free energy barriers are 7.62 ± 0.11, 9.15 ± 0.16, and 11.63 ± 0.11 kcal mol^-1 for the ZZ, AC, and bulk sites. The interlink between both the mechanisms is established concerning the reported free energy barriers for OOH* formation. The ZZ site is found to lower the activation barrier for the rate-determining step, followed by the AC and bulk.

Reconstructing Reactivity in Dynamic Host-Guest Systems at Atomistic Resolution: Amide Hydrolysis Under Confinement in the Cavity of a Coordination Cage

Spatial confinement is widely employed by nature to attain unique efficiency in controlling chemical reactions. Notable examples are enzymes, which selectively bind reactants and exquisitely regulate their conversion into products. In an attempt to mimic natural catalytic systems, supramolecular metal–organic cages capable of encapsulating guests in their cavity and of controlling/accelerating chemical reactions under confinement are attracting increasing interest. However, the complex nature of these systems, where reactants/products continuously exchange in-and-out of the host, makes it often difficult to elucidate the factors controlling the reactivity in dynamic regimes. As a case study, here we focus on a coordination cage that can encapsulate amide guests and enhance their hydrolysis by favoring their mechanical twisting towards reactive molecular configurations under confinement. We designed an advanced multiscale simulation approach that allows us to reconstruct the reactivity in such host–guest systems in dynamic regimes. In this way, we can characterize amide encapsulation/expulsion in/out of the cage cavity (thermodynamics and kinetics), coupling such host–guest dynamic equilibrium with characteristic hydrolysis reaction constants. All computed kinetic/thermodynamic data are then combined, obtaining a statistical estimation of reaction acceleration in the host–guest system that is found in optimal agreement with the available experimental trends. This shows how, to understand the key factors controlling accelerations/variations in the reaction under confinement, it is necessary to take into account all dynamic processes that occur as intimately entangled in such host–guest systems. This also provides us with a flexible computational framework, useful to build structure–dynamics–property relationships for a variety of reactive host–guest systems.

Encapsulation of guests in metal–organic cages allows control over chemical reactivity. Focusing on the hydrolysis of amides, here we show an effective molecular simulation approach to reconstruct reactivity in host–guest systems in dynamic regimes.