CoeffNet : predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network
CoeffNet uses coefficients of molecular orbitals of reactants and products to predict activation barriers.
Engineering topics
Publications and source records attributed to Vijay, Sudarshan.
CoeffNet uses coefficients of molecular orbitals of reactants and products to predict activation barriers.
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A new infrastructure for computed molecular properties, including a web app and API, is incorporated into the Materials Project, enabling the creation of a database currently containing over 170 000 molecules.
Kinetic models parameterized by ab-initio calculations have led to significant improvements in understanding chemical reactions in heterogeneous catalysis. These studies have been facilitated by implementations which determine steady-state coverages and rates of mean-field micro-kinetic models. As implemented in the open-source kinetic modeling program, CatMAP, the conventional solution strategy is to use a root-finding algorithm to determine the coverage of all intermediates through the steady-state expressions, constraining all coverages to be non-negative and to properly sum to unity. Though intuitive, this root-finding strategy causes issues with convergence to solution due to these imposed constraints. In this work, we avoid explicitly imposing these constraints, solving the mean-field steady-state micro-kinetic model in the space of number of sites instead of solving it in the space of coverages. Here, we transform the constrained root-finding problem to an unconstrained least-squares minimization problem, leading to significantly improved convergence in solving micro-kinetic models and thus enabling the efficient study of more complex catalytic reactions.
Metal–water interfaces are central to understanding aqueous-phase heterogeneous catalytic processes. However, the explicit modeling of the interface is still challenging as it necessitates extensive sampling of the interfaces’ degrees of freedom. Herein, we use ab initio molecular dynamics (AIMD) simulations to study the adsorption of furfural, a platform biomass chemical on several catalytically relevant metal–water interfaces (Pt, Rh, Pd, Cu, and Au) at low coverages. We find that furfural adsorption is destabilized on all the metal–water interfaces compared to the metal–gas interfaces considered in this work. This destabilization is a result of the energetic penalty associated with the displacement of water molecules near the surface upon adsorption of furfural, further evidenced by a linear correlation between solvation energy and the change in surface water coverage. To predict solvation energies without the need for computationally expensive AIMD simulations, we demonstrate OH binding energy as a good descriptor to estimate the solvation energies of furfural. Using microkinetic modeling, we further explain the origin of the activity for furfural hydrogenation on intrinsically strong-binding metals under aqueous conditions, i.e., the endothermic solvation energies for furfural adsorption prevent surface poisoning. In conclusion, our work sheds light on the development of active aqueous-phase catalytic systems via rationally tuning the solvation energies of reaction intermediates.
Through the use of high-throughput quantum-mechanical calculations, we uncover a diverse range of metal alloys and intermetallics with “free-atom-like” electronic states and discuss the potential implications for heterogeneous catalyst design.
Electrochemical CO 2 reduction is a potential approach to convert CO 2 into valuable chemicals using electricity as feedstock. Abundant and affordable catalyst materials are needed to upscale this process in a sustainable manner. Nickel-nitrogen-doped carbon (Ni-N-C) is an efficient catalyst for CO 2 reduction to CO, and the single-site Ni-N x motif is believed to be the active site. However, critical metrics for its catalytic activity, such as active site density and intrinsic turnover frequency, so far lack systematic discussion. In this work, we prepared a set of covalent organic framework (COF)-derived Ni-N-C catalysts, for which the Ni-N x content could be adjusted by the pyrolysis temperature. The combination of high-angle annular dark-field scanning transmission electron microscopy and extended X-ray absorption fine structure evidenced the presence of Ni single-sites, and quantitative X-ray photoemission addressed the relation between active site density and turnover frequency.