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We present a new efficient transition pathway search method based on the least action principle and the Gaussian process regression method. Most pathway search methods developed so far rely on string representations, which approximate a transition pathway by a series of slowly varying replicas of a system. Since those methods require a large number of replica images, they are computationally expensive in general. Our approach employs the Gaussian process regression method, which takes the Bayesian inference on the shape of a given potential energy surface with a few observed data and Gaussian-shaped kernel functions. Based on the inferred potential, we find multiple low-action pathways by carrying out the action optimization based on the Action-CSA (Conformational space annealing). Here we demonstrate a drastic elevation of computing efficiency about five orders of magnitude for the system with the Muller-Brown potential. Further, for the sake of demonstrating its real-world capabilities, we apply our method to ab initio calculations on alanine dipeptide. The improved efficiency of GPAO makes it possible to identify multiple transition pathways of alanine dipeptide and calculate their transition probabilities with ab initio accuracy. We are confident that our GPAO method is a powerful approach to investigate the mechanisms of complex chemical reactions
Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerica
We study a simple group chase and escape model by introducing new parameters with which configurations of chasing and escaping in groups are classified into three characteristic patterns. In particular, the parameters distinguish two essential config
Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, espec
Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emula
We present an approach to construct appropriate and efficient emulators for Hamiltonian flow maps. Intended future applications are long-term tracing of fast charged particles in accelerators and magnetic plasma confinement configurations. The method