Berlin 2024 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 44: Developement of Calculation Methods II
MM 44.5: Talk
Wednesday, March 20, 2024, 16:45–17:00, C 264
Improving the Diversity of Transition State Searches with On-the-fly Learned Biasing Potentials — •Nils Gönnheimer1,2, King Chun Lai1, Karsten Reuter1, and Johannes T. Margraf1,2 — 1Fritz-Haber-Institut der MPG Berlin — 2Universität Bayreuth
Constructing reaction networks of reaction pathways is fundamental for long-timescale simulations and the theoretical analysis of complex chemical processes. A common approach involves using minimum mode following (MMF) methods to identify these reaction pathways, and especially transition states (TSs), on potential energy surfaces (PESs). However, MMF methods may miss essential reaction pathways due to their tendency of converging towards a limited set of transition states, even when simulations are repeated with different starting conditions. Herein, we address this limitation by introducing a biasing potential which modifies the PES based on on-the-fly gathered information. The bias drives the TS search algorithm away from known TSs and therefore increases the diversity of the outcome. We demonstrate the impact of the on-the-fly generated bias on the MMF method by showing the change of basins of attraction for first-order saddle points within a benchmark 2D PES. Furthermore, we will illustrate the effect of the bias on the diversity of identified TSs during a restricted number of TS searches for the self-diffusion of a heptamer on a Pt(111) surface.
Keywords: Transition state search; Minimum mode following; Bias potentials