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Biomedical subjects

Steven K Burger

Publications and source records attributed to Steven K Burger.

2 recordsLinked to original sources

A combined explicit-implicit method for high accuracy reaction path integration.

We present the use of an optimal combined explicit-implicit method for following the reaction path to high accuracy. This is in contrast to most purely implicit reaction path integration algorithms, which are only efficient on stiff ordinary differential equations. The defining equation for the reaction path is considered to be stiff, however, we show here that the reaction path is not uniformly stiff and instead is only stiff near stationary points. The optimal algorithm developed in this work is a combination of explicit and implicit methods with a simple criterion to switch between the two. Using three different chemical reactions, we combine and compare three different integration methods: the implicit trapezoidal method, an explicit stabilized third order algorithm implemented in the code DUMKA3 and the traditional explicit fourth order Runge-Kutta method written in the code RKSUITE. The results for high accuracy show that when the implicit trapezoidal method is combined with either explicit method the number of energy and gradient calculations can potentially be reduced by almost a half compared with integrating either method alone. Finally, to explain the improvements of the combined method we expand on the concepts of stability and stiffness and relate them to the efficiency of integration methods.

Acetaldehyde↗

Quadratic string method for determining the minimum-energy path based on multiobjective optimization.

Based on a multiobjective optimization framework, we develop a new quadratic string method for finding the minimum-energy path. In the method, each point on the minimum-energy path is minimized by integration in the descent direction perpendicular to path. Each local integration is done on a quadratic surface approximated by a damped Broyden-Fletcher-Goldfarb-Shanno updated Hessian, allowing the algorithm to take many steps between energy and gradient calls. The integration is performed with an adaptive step-size solver, which is restricted in length to the trust radius of the approximate Hessian. The full algorithm is shown to be capable of practical superlinear convergence, in contrast to the linear convergence of other methods. The method also eliminates the need for predetermining such parameters as step size and spring constants, and is applicable to reactions with multiple barriers. The effectiveness of this method is demonstrated for the Muller-Brown potential, a seven-atom Lennard-Jones cluster, and the enolation of acetaldehyde to vinyl alcohol.

Acetaldehyde↗