Optimal Scheme to Achieve Energy Conservation in Induced Dipole Models

Induced dipole models have proven to be effective toolsfor simulatingelectronic polarization effects in biochemical processes, yet theirpotential has been constrained by energy conservation issue, particularlywhen historical data is utilized for dipole prediction. This studyidentifies error outliers as the primary factor causing this failureof energy conservation and proposes a comprehensive scheme to overcomethis limitation. Leveraging maximum relative errors as a convergencemetric, our data demonstrates that energy conservation can be upheldeven when using historical information for dipole predictions. Ourstudy introduces the multi-order extrapolation method to quicken inductioniteration and optimize the use of historical data, while also developingthe preconditioned conjugate gradient with local iterations to refinethe iteration process and effectively remove error outliers. Thisscheme further incorporates a peek step via Jacobiunder-relaxation for optimal performance. Simulation evidence suggeststhat our proposed scheme can achieve energy convergence akin to thatof point-charge models within a limited number of iterations, thuspromising significant improvements in efficiency and accuracy.

Automated summary

Induced dipole models have been limited by the energy conservation issue when utilizing historical data. This study identifies error outliers as the primary factor causing energy conservation failure and proposes a comprehensive scheme to overcome this limitation. The scheme incorporates a multi-order extrapolation method to quicken induction iteration, a preconditioned conjugate gradient with local iterations to refine the iteration process, and a peek step via Jacobi under-relaxation for optimal performance. Simulation evidence suggests that this proposed scheme achieves energy convergence similar to point-charge models with improved efficiency and accuracy.