No Arabic abstract
Finding complex reaction and transformation pathways, involving many intermediate states, is in general not possible on the DFT level with existing simulation methods due to the very large number of required energy and force evaluations. This is due to a large extent to the fact that for complex reactions, it is not possible to determine which atom in the educt is mapped onto which atom in the product. Trying out all possible atomic index mappings is not feasible because of the factorial increase in the number of possible mappings. By using a penalty function that is invariant under index permutations, we can bias the potential energy surface in such a way that it obtains the characteristics of a structure seeker whose global minimum is the product. By performing a Minima Hopping based global optimization on this biased potential energy surface we can rapidly find intermediate states that lead into the global minimum. Based on this information we can then extract the full reaction pathway. We first demonstrate for a benchmark system, namely LJ38 that our method allows to preferentially find intermediate states that are relevant for the lowest energy reaction pathway and that we, therefore, need a much smaller number of intermediate states than previous methods to find the lowest energy reaction pathway. Finally, we apply the method to two real systems, C60 and C20H20 and show that the found reaction pathway contains valuable information on how the system can be synthesized.
Equilibrium atomic configurations and electron energy structure of ethanol adsorbed on the Si (111) surface are studied by the first-principles density functional theory. Geometry optimization is performed by the total energy minimization method. Several equilibrium atomic configurations of ethanol, both undissociated and dissociated, on the Si (111) surface are found. Reaction pathways and predicted transition states are discussed in comparison with available experimental data in terms of the feasibility of the reactions occurring. Analysis of atom and orbital resolved projected density of states indicate substantial modifications of the Si surface valence and conduction bands due to the adsorption of ethanol affecting the electrical properties of the surface.
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences, and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sampling based estimator, however, FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions. One strategy to mitigate this problem, called Targeted Free Energy Perturbation, uses a high-dimensional mapping in configuration space to increase overlap of the underlying distributions. Despite its potential, this method has attracted only limited attention due to the formidable challenge of formulating a tractable mapping. Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase overlap. We develop a new model architecture that respects permutational and periodic symmetries often encountered in atomistic simulations and test our method on a fully-periodic solvation system. We demonstrate that our method leads to a substantial variance reduction in free energy estimates when compared against baselines, without requiring any additional data.
Rare transitions between long-lived stable states are often analyzed in terms of free energy landscapes computed as functions of a few collective variables. Here, using transitions between geometric phases as example, we demonstrate that the effective dynamics of a system along these variables are an essential ingredient in the description of rare events and that the static perspective provided by the free energy alone may be misleading. In particular, we investigate the disk-to-slab transition in the two-dimensional Ising model starting with a calculation of a two-dimensional free energy landscape and the distribution of committor probabilities. While at first sight it appears that the committor is incompatible with the free energy, they can be reconciled with each other using a two-dimensional Smoluchowski equation that combines the free energy landscape with state dependent diffusion coefficients. These results illustrate that dynamical information is not only required to calculate rate constants but that neglecting dynamics may also lead to an inaccurate understanding of the mechanism of a given process.
We consider stochastic models of chemical reaction networks with time dependent input rates and several types of molecules. We prove that, in despite of strong time dependence of input rates, there is a kind of homeostasis phenomenon: far away from input nodes the mean numbers of molecules of each type become approximately constant (do not depend on time).
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting fermions in 1d, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. Challenges for application of our method to real electronic structure problems are discussed.