Using Hessian update formulae to construct modified Shepard interpolated potential energy surfaces: Application to vibrating surface atoms

Quantum dynamics of excited state proton transfer in green fluorescent protein

Photoexcitation of green fluorescent protein (GFP) triggers long-range proton transfer along a "wire" of neighboring protein residues, which, in turn, activates its characteristic green fluorescence. The GFP proton wire is one of the simplest, most well-characterized models of biological proton transfer but remains challenging to simulate due to the sensitivity of its energetics to the surrounding protein conformation and the possibility of non-classical behavior associated with the movement of lightweight protons. Using a direct dynamics variational multiconfigurational Gaussian wavepacket method to provide a fully quantum description of both electrons and nuclei, we explore the mechanism of excited state proton transfer in a high-dimensional model of the GFP chromophore cluster over the first two picoseconds following excitation. During our simulation, we observe the sequential starts of two of the three proton transfers along the wire, confirming the predictions of previous studies that the overall process starts from the end of the wire furthest from the fluorescent chromophore and proceeds in a concerted but asynchronous manner. Furthermore, by comparing the full quantum dynamics to a set of classical trajectories, we provide unambiguous evidence that tunneling plays a critical role in facilitating the leading proton transfer.

Assessing and rationalizing the performance of Hessian update schemes for reaction path Hamiltonian rate calculations

The reaction path Hamiltonian (RPH) can be used to calculate chemical reaction rate constants, going beyond transition-state theory in taking account of recrossing by providing an approximation to the dynamic transmission coefficient. However, the RPH necessitates the calculation of the Hessian matrix at a number of points along the minimum energy path; the associated computational cost stands as a bottleneck in RPH calculations, especially if one is interested in using high-accuracy electronic structure methods. In this work, four different Hessian update schemes (symmetric rank-1, Powell-symmetric Broyden, Bofill, and TS-BFGS updates) are assessed to see whether or not they reliably reproduce calculated transmission coefficients for three different chemical reactions. Based on the reactions investigated, the symmetric rank-1 Hessian update was the least appropriate for RPH construction, giving different transmission coefficients from the standard analytical Hessian approach, as well as inconsistent frequencies and coupling properties. The Bofill scheme, the Powell-symmetric Broyden scheme, and the TS-BFGS scheme were the most reliable Hessian update methods, with transmission coefficients that were in good agreement with those calculated by the standard RPH calculations. The relative accuracy of the different Hessian update schemes is further rationalized by investigating the approximated Coriolis and curvature coupling terms along the reaction-path, providing insight into when these schemes would be expected to work well. Furthermore, the associated computational cost associated with the RPH calculations was substantially reduced by the tested update schemes. Together, these results provide useful rules-of-thumb for using Hessian update schemes in RPH simulations.

Computational approaches to dissociative chemisorption on metals: towards chemical accuracy

We review the state-of-the-art in the theory of dissociative chemisorption (DC) of small gas phase molecules on metal surfaces, which is important to modeling heterogeneous catalysis for practical reasons, and for achieving an understanding of the wealth of experimental information that exists for this topic, for fundamental reasons. We first give a quick overview of the experimental state of the field. Turning to the theory, we address the challenge that barrier heights (E_b, which are not observables) for DC on metals cannot yet be calculated with chemical accuracy, although embedded correlated wave function theory and diffusion Monte-Carlo are moving in this direction. For benchmarking, at present chemically accurate E_b can only be derived from dynamics calculations based on a semi-empirically derived density functional (DF), by computing a sticking curve and demonstrating that it is shifted from the curve measured in a supersonic beam experiment by no more than 1 kcal mol^-1. The approach capable of delivering this accuracy is called the specific reaction parameter (SRP) approach to density functional theory (DFT). SRP-DFT relies on DFT and on dynamics calculations, which are most efficiently performed if a potential energy surface (PES) is available. We therefore present a brief review of the DFs that now exist, also considering their performance on databases for E_b for gas phase reactions and DC on metals, and for adsorption to metals. We also consider expressions for SRP-DFs and briefly discuss other electronic structure methods that have addressed the interaction of molecules with metal surfaces. An overview is presented of dynamical models, which make a distinction as to whether or not, and which dissipative channels are modeled, the dissipative channels being surface phonons and electronically non-adiabatic channels such as electron-hole pair excitation. We also discuss the dynamical methods that have been used, such as the quasi-classical trajectory method and quantum dynamical methods like the time-dependent wave packet method and the reaction path Hamiltonian method. Limits on the accuracy of these methods are discussed for DC of diatomic and polyatomic molecules on metal surfaces, paying particular attention to reduced dimensionality approximations that still have to be invoked in wave packet calculations on polyatomic molecules like CH₄. We also address the accuracy of fitting methods, such as recent machine learning methods (like neural network methods) and the corrugation reducing procedure. In discussing the calculation of observables we emphasize the importance of modeling the properties of the supersonic beams in simulating the sticking probability curves measured in the associated experiments. We show that chemically accurate barrier heights have now been extracted for DC in 11 molecule-metal surface systems, some of which form the most accurate core of the only existing database of E_b for DC reactions on metal surfaces (SBH10). The SRP-DFs (or candidate SRP-DFs) that have been derived show transferability in many cases, i.e., they have been shown also to yield chemically accurate E_b for chemically related systems. This can in principle be exploited in simulating rates of catalyzed reactions on nano-particles containing facets and edges, as SRP-DFs may be transferable among systems in which a molecule dissociates on low index and stepped surfaces of the same metal. In many instances SRP-DFs have allowed important conclusions regarding the mechanisms underlying observed experimental trends. An important recent observation is that SRP-DFT based on semi-local exchange DFs has so far only been successful for systems for which the difference of the metal work function and the molecule's electron affinity exceeds 7 eV. A main challenge to SRP-DFT is to extend its applicability to the other systems, which involve a range of important DC reactions of e.g. O₂, H₂O, NH₃, CO₂, and CH₃OH. Recent calculations employing a PES based on a screened hybrid exchange functional suggest that the road to success may be based on using exchange functionals of this category.

PySurf: A Framework for Database Accelerated Direct Dynamics

The greatest restriction to the theoretical study of the dynamics of photoinduced processes is computationally expensive electronic structure calculations. Machine learning algorithms have the potential to reduce the number of these computations significantly. Here, PySurf is introduced as an innovative code framework, which is specifically designed for rapid prototyping and development tasks for data science applications in computational chemistry. It comes with powerful Plugin and Workflow engines, which allows intuitive customization for individual tasks. Data is automatically stored through the database framework, which enables additional interpolation of properties in previously evaluated regions of the conformational space. To illustrate the potential of the framework, a code for nonadiabatic surface hopping simulations based on the Landau-Zener algorithm is presented here. Deriving gradients from the interpolated potential energy surfaces allows for full-dimensional nonadiabatic surface hopping simulations using only adiabatic energies (energy only). Simulations of a pyrazine model and ab initio-based calculations of the SO2 molecule show that energy-only calculations with PySurf are able to correctly predict the nonadiabatic dynamics of these prototype systems. The results reveal the degree of sophistication, which can be achieved by the database accelerated energy-only surface hopping simulations being competitive to commonly used semiclassical approaches.

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.

Curve crossing in a manifold of coupled electronic states: direct quantum dynamics simulations of formamide

Quantum dynamics simulations are an important tool to evaluate molecular behaviour including the, often key, quantum nature of the system. In this paper we present an algorithm that is able to simulate the time evolution of a molecule after photo-excitation into a manifold of states. The direct dynamics variational multi-configurational Gaussian (DD-vMCG) method circumvents the computational bottleneck problems of traditional grid-based methods by computing the potential energy functions on-the-fly, i.e. only where required. Unlike other commonly used direct dynamics methods, DD-vMCG is fully quantum mechanical. Here, the method is combined with a novel on-the-fly diabatisation scheme to simulate the short-time dynamics of the key molecule formamide and its acid analogue formimidic acid. This is a challenging test system due to the nature and large number of excited states, and eight coupled states are included in the calculations. It is shown that the method is able to provide unbiased information on the product channels open after excitation at different energies and demonstrates the potential to be a practical scheme, limited mainly by the quality of the quantum chemistry used to describe the excited states.

Accelerating direct quantum dynamics using graphical processing units

The direct dynamics variational multi-configurational Gaussian (DD-vMCG) method is combined with electronic structure calculations accelerated by Graphical Processing Units (GPUs). This is used to identify GPU acceleration will have a significant effect for both ground and excited state simulations.