Solvation Free Energies from Machine Learning Molecular Dynamics.

The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions.

Mechanistic Insights on Functionalization of Graphene with Ozone.

The exposure of graphene to O3 results in functionalization of its lattice with epoxy, even at room temperature. This reaction is of fundamental interest for precise lattice patterning, however, is not well understood. Herein, using van der Waals density functional theory (vdW-DFT) incorporating spin-polarized calculations, we find that O3 strongly physisorbs on graphene with a binding energy of -0.46 eV. It configures in a tilted position with the two terminal O atoms centered above the neighboring graphene honeycombs. A dissociative chemisorption follows by surpassing an energy barrier of 0.75 eV and grafting an epoxy group on graphene reducing the energy of the system by 0.14 eV from the physisorbed state. Subsequent O3 chemisorption is preferred on the same honeycomb, yielding two epoxy groups separated by a single C-C bridge. We show that capturing the onset of spin in oxygen during chemisorption is crucial. We verify this finding with experiments where an exponential increase in the density of epoxy groups as a function of reaction temperature yields an energy barrier of 0.66 eV, in agreement with the DFT prediction. These insights will help efforts to obtain precise patterning of the graphene lattice.

Static dielectric permittivity of ice from first principles.

The static permittivity of ice is computed from first principles as a function of the electric field, together with the generalized Kirkwood factor. The molecular dipole in ice is unambiguously obtained by an original method combining a slab approach and Berry phase calculations, and the fluctuations of the polarization are sampled by Monte Carlo runs using first-principles model Hamiltonians for different proton configurations. Common approximations in the exchange-correlation functionals overestimate the dielectric permittivity and enhance ferroelectric configurations and the Kirkwood factor, whereas dielectric saturation effects compare well with experiment.

Effect of thermal motion on catalytic activity of nanoparticles in polar solvent.

In this study, we propose that electrode potential fluctuations due to the thermal motion of the solvent may serve to enhance the catalytic activity of nanostructures. The proposed model uses a simple, Marcus-type treatment of the statistical behavior of the solvent and the Butler-Volmer law for the instantaneous catalytic rate as a function of the electrode potential. The rapid development of probing techniques with high spatial and temporal resolution will help to further confirm and characterize the dynamical properties of nanostructures.

Surface composition tuning of Au-Pt bimetallic nanoparticles for enhanced carbon monoxide and methanol electro-oxidation.

The ability to direct bimetallic nanoparticles to express desirable surface composition is a crucial step toward effective heterogeneous catalysis, sensing, and bionanotechnology applications. Here we report surface composition tuning of bimetallic Au-Pt electrocatalysts for carbon monoxide and methanol oxidation reactions. We establish a direct correlation between the surface composition of Au-Pt nanoparticles and their catalytic activities. We find that the intrinsic activities of Au-Pt nanoparticles with the same bulk composition of Au0.5Pt0.5 can be enhanced by orders of magnitude by simply controlling the surface composition. We attribute this enhancement to the weakened CO binding on Pt in discrete Pt or Pt-rich clusters surrounded by surface Au atoms. Our finding demonstrates the importance of surface composition control at the nanoscale in harnessing the true electrocatalytic potential of bimetallic nanoparticles and opens up strategies for the development of highly active bimetallic nanoparticles for electrochemical energy conversion.

First-principles prediction of the equilibrium shape of nanoparticles under realistic electrochemical conditions.

A first-principles model of the electrochemical double layer is applied to study surface energies and surface coverage under realistic electrochemical conditions and to determine the equilibrium shape of metal nanoparticles as a function of applied potential. The potential bias is directly controlled by adding electronic charge to the system, while total energy calculations and thermodynamic relations are used to predict electrodeposition curves and changes in surface energies and coverage. This approach is applied to Pt surfaces subject to hydrogen underpotential deposition. The shape of Pt nanoparticles under a cathodic scan is shown to undergo an octahedric-to-cubic transition, which is more pronounced in alkaline media due to the interaction energy of the pH-dependent surface charge with the surface dipole.

First-principles molecular dynamics at a constant electrode potential.

A simulation scheme for performing first-principles molecular dynamics at a constant electrode potential is presented, opening the way for a more realistic modeling of voltage-driven devices. The system is allowed to exchange electrons with a reservoir at fixed potential, and dynamical equations for the total electronic charge are derived by using the potential energy of the extended system. In combination with a thermostat, this potentiostat scheme reproduces thermal fluctuations of the charge with the correct statistics, implying a realistic treatment of the potential as a control variable. Practically, the dynamics of the charge are decoupled from the electronic structure calculations, making the scheme easily implementable in existing first-principles molecular dynamics codes. Our approach is demonstrated on a test system by considering various test cases.