Litcius/Paper detail

Grand Canonical Ensemble Modeling of Electrochemical Interfaces Made Simple

Zhaoming Xia, Hai Xiao

2023Journal of Chemical Theory and Computation70 citationsDOI

Abstract

Grand canonical ensemble (GCE) modeling of electrochemical interfaces, in which the electrochemical potential is converged to a preset constant, is essential for understanding electrochemistry and electrocatalysis at the electrodes. However, it requires developing efficient and robust algorithms to perform practical and effective GCE modeling with density functional theory (DFT) calculations. Herein, we developed an efficient and robust fully converged constant-potential (FCP) algorithm based on Newton's method and a polynomial fitting to calculate the necessary derivative for DFT calculations. We demonstrated with the constant-potential geometry optimization and Born-Oppenheimer molecular dynamics (BOMD) calculations that our FCP algorithm is resistant to the numerical instability that plagues other algorithms, and it delivers efficient convergence to the preset electrochemical potential and renders accurate forces for updating the nuclear positions of an electronically open system, outperforming other algorithms. The implementation of our FCP algorithm enables flexibility in using various computational codes and versatility in performing advanced tasks including the constant-potential enhanced-sampling BOMD simulations that we showcased with the modeling of the electrochemical hydrogenation of CO, and it is thus expected to find a wide spectrum of applications in the modeling of chemistry at electrochemical interfaces.

Topics & Concepts

Computer scienceMolecular dynamicsCanonical ensembleConstant (computer programming)Convergence (economics)ElectrochemistryDensity functional theoryFlexibility (engineering)Force constantStability (learning theory)AlgorithmStatistical physicsComputational chemistryComputational scienceChemistryPhysical chemistryPhysicsMoleculeElectrodeMathematicsProgramming languageOrganic chemistryStatisticsMonte Carlo methodMachine learningEconomic growthEconomicsMachine Learning in Materials ScienceCO2 Reduction Techniques and CatalystsSpectroscopy and Quantum Chemical Studies