title: Finding mechanochemical pathways and barriers without transition state search.
abstract: In covalent mechanochemistry, precise application of mechanical stress to molecules of interest ("mechanophores") is used to induce to promote desired reaction pathways. Computational prediction of such phenomena and rational mechanophore design involves the computationally costly task of finding relevant transition-state saddles on force-deformed molecular potential energy surfaces (PESs). Finding a transition state often requires an initial guess about the pathway by which the reaction will proceed. Unfortunately, chemical intuition often fails when predicting likely consequences of mechanical stress applied to molecular systems. Here, we describe a fully deterministic method for finding mechanochemically relevant transition states and reaction pathways. The method is based on the observation that application of a sufficiently high mechanical force will eventually destabilize any molecular structure. Mathematically, such destabilization proceeds via a "catastrophe" occurring at a critical force where the energy minimum corresponding to the stable molecular structure coalesces with a transition state. Catastrophe theory predicts the force-deformed PES to have universal behavior in the vicinity of the critical force, allowing us to deduce the molecular structure of the transition state just below the critical force analytically. We then use the previously developed method of tracking transition-state evolution with the force to map out the entire reaction path and to predict the complete force dependence of the reaction barrier. Beyond its applications in mechanochemistry, this approach may be useful as a general method of finding transition states using fictitious forces to target specific reaction mechanisms.
authors.affiliation: Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
authors.forename: Stanislav M
authors.initials: SM
authors.lastname: Avdoshenko
authors.affiliation: Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
authors.forename: Dmitrii E
authors.initials: DE
authors.lastname: Makarov