Locating transition states on potential energy surfaces by the gentlest ascent dynamics

Theory and Examples of Catch Bonds

We discuss slip bonds, catch bonds, and the tug-of-war mechanism using mathematical arguments. The aim is to explain the theoretical tool of molecular potential energy surfaces (PESs). For this, we propose simple 2-dimensional surface models to demonstrate how a molecule under an external force behaves. Examples are selectins. Catch bonds, in particular, are explained in more detail, and they are contrasted to slip bonds. We can support special two-dimensional molecular PESs for E- and L-selectin which allow the catch bond property. We demonstrate that Newton trajectories (NT) are powerful tools to describe these phenomena. NTs form the theoretical background of mechanochemistry.

Locating saddle points using gradient extremals on manifolds adaptively revealed as point clouds

Steady states are invaluable in the study of dynamical systems. High-dimensional dynamical systems, due to separation of time scales, often evolve toward a lower dimensional manifold M. We introduce an approach to locate saddle points (and other fixed points) that utilizes gradient extremals on such a priori unknown (Riemannian) manifolds, defined by adaptively sampled point clouds, with local coordinates discovered on-the-fly through manifold learning. The technique, which efficiently biases the dynamical system along a curve (as opposed to exhaustively exploring the state space), requires knowledge of a single minimum and the ability to sample around an arbitrary point. We demonstrate the effectiveness of the technique on the Müller-Brown potential mapped onto an unknown surface (namely, a sphere). Previous work employed a similar algorithmic framework to find saddle points using Newton trajectories and gentlest ascent dynamics; we, therefore, also offer a brief comparison with these methods.

Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials

Collective variable-based enhanced sampling methods are routinely used on systems with metastable states, where high free energy barriers impede the proper sampling of the free energy landscapes when using conventional molecular dynamics simulations. One such method is variationally enhanced sampling (VES), which is based on a variational principle where a bias potential in the space of some chosen slow degrees of freedom, or collective variables, is constructed by minimizing a convex functional. In practice, the bias potential is taken as a linear expansion in some basis function set. So far, primarily basis functions delocalized in the collective variable space, like plane waves, Chebyshev, or Legendre polynomials, have been used. However, there has not been an extensive study of how the convergence behavior is affected by the choice of the basis functions. In particular, it remains an open question if localized basis functions might perform better. In this work, we implement, tune, and validate Daubechies wavelets as basis functions for VES. The wavelets construct orthogonal and localized bases that exhibit an attractive multiresolution property. We evaluate the performance of wavelet and other basis functions on various systems, going from model potentials to the calcium carbonate association process in water. We observe that wavelets exhibit excellent performance and much more robust convergence behavior than all other basis functions, as well as better performance than metadynamics. In particular, using wavelet bases yields far smaller fluctuations of the bias potential within individual runs and smaller differences between independent runs. Based on our overall results, we can recommend wavelets as basis functions for VES.

Generative adversarial networks for transition state geometry prediction

This work introduces a novel application of generative adversarial networks (GANs) for the prediction of starting geometries in transition state (TS) searches based on the geometries of reactants and products. The multi-dimensional potential energy space of a chemical reaction often complicates the location of a starting TS geometry, leading to the correct TS combining reactants and products in question. The proposed TS-GAN efficiently maps the space between reactants and products and generates reliable TS guess geometries, and it can be easily combined with any quantum chemical software package performing geometry optimizations. The TS-GAN was trained and applied to generate TS guess structures for typical chemical reactions, such as hydrogen migration, isomerization, and transition metal-catalyzed reactions. The performance of the TS-GAN was directly compared to that of classical approaches, proving its high accuracy and efficiency. The current TS-GAN can be extended to any dataset that contains sufficient chemical reactions for training. The software is freely available for training, experimentation, and prediction at https://github.com/ekraka/TS-GAN.

ALTRUISM: A Higher Calling

A new robust surface-walking algorithm for locating transition states is presented. By modifying the Trust-Region Image Surface Minimization method to walk to higher-order stationary points, where the Hessian has more than one negative eigenvalue, we have developed an approach that determines transition states from above, by walking downhill on the potential energy surface. We call this algorithm "ALTRUISM"-Alpine Trust-region Image Surface Method. We test the performance of the approach by applying it to a range of systems with several different quantum-chemical methods. Our results demonstrate that ALTRUISM is a systematic, robust, and unbiased method for connecting a reactant and a product via a transition state. The algorithmic combination of walking uphill (to different higher-order stationary points) and downhill to saddle points enables us to explore qualitatively distinct reaction pathways and construct a network of elementary reactions on a given potential energy surface.

Interplay between the Gentlest Ascent Dynamics Method and Conjugate Directions to Locate Transition States

An algorithm to locate transition states on a potential energy surface (PES) is proposed and described. The technique is based on the GAD method where the gradient of the PES is projected into a given direction and also perpendicular to it. In the proposed method, named GAD-CD, the projection is not only applied to the gradient but also to the Hessian matrix. Then, the resulting Hessian matrix is block diagonal. The direction is updated according to the GAD method. Furthermore, to ensure stability and to avoid a high computational cost, a trust region technique is incorporated and the Hessian matrix is updated at each iteration. The performance of the algorithm in comparison with the standard ascent dynamics is discussed for a simple two dimensional model PES. Its efficiency for describing the reaction mechanisms involving small and medium size molecular systems is demonstrated for five molecular systems of interest.

The role of NH3 and hydrocarbon mixtures in GaN pseudo-halide CVD: a quantum chemical study

The prospects of a control for a novel gallium nitride pseudo-halide vapor phase epitaxy (PHVPE) with HCN were thoroughly analyzed for hydrocarbons-NH3-Ga gas phase on the basis of quantum chemical investigation with DFT (B3LYP, B3LYP with D3 empirical correction on dispersion interaction) and ab-initio (CASSCF, coupled clusters, and multireference configuration interaction including MRCI+Q) methods. The computational screening of reactions for different hydrocarbons (CH4, C2H6, C3H8, C2H4, and C2H2) as readily available carbon precursors for HCN formation, potential chemical transport agents, and for controlled carbon doping of deposited GaN was carried out with the B3LYP method in conjunction with basis sets up to aug-cc-pVTZ. The gas phase intermediates for the reactions in the Ga-hydrocarbon systems were predicted at different theory levels. The located π-complexes Ga…C2H2 and Ga…C2H4 were studied to determine a probable catalytic activity in reactions with NH3. A limited influence of the carbon-containing atmosphere was exhibited for the carbon doping of GaN crystal in the conventional GaN chemical vapor deposition (CVD) process with hydrocarbons injected in the gas phase. Our results provide a basis for experimental studies of GaN crystal growth with C2H4 and C2H2 as auxiliary carbon reagents for the Ga-NH3 and Ga-C-NH3 CVD systems and prerequisites for reactor design to enhance and control the PHVPE process through the HCN synthesis.

Reaction Path Following with Sparse Interpolation

Computing the potential energy of an N-atom molecule is an expensive optimization process of 3N - 6 molecular coordinates, so following reaction pathways as a function of all 3N - 6 coordinates is unfeasible for large molecules. In this paper, we present a method that isolates d < 3N - 6 molecular coordinates and continuously follows reaction paths on d-dimensional potential energy surfaces approximated by a Smolyak's sparse grid interpolation algorithm.1 Compared to dense grids, sparse grids efficiently improve the ratio of invested storage and computing time to approximation accuracy and thus allow one to increase the number of coordinates d in molecular reaction path following simulations. Furthermore, evaluation of the interpolant is much less expensive than the evaluation of the actual energy function, so our technique offers a computationally efficient way to simulate reaction paths on ground and excited state potential energy surfaces. To demonstrate the capabilities of our method, we present simulation results for the isomerization of 2-butene with two, three, and six degrees of freedom.