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Geometry Optimization with Machine Trained Topological Atoms.

ABSTRACT: The geometry optimization of a water molecule with a novel type of energy function called FFLUX is presented, which bypasses the traditional bonded potentials. Instead, topologically-partitioned atomic energies are trained by the machine learning method kriging to predict their IQA atomic energies for a previously unseen molecular geometry. Proof-of-concept that FFLUX's architecture is suitable for geometry optimization is rigorously demonstrated. It is found that accurate kriging models can optimize 2000 distorted geometries to within 0.28?kJ?mol-1 of the corresponding ab initio energy, and 50% of those to within 0.05?kJ?mol-1. Kriging models are robust enough to optimize the molecular geometry to sub-noise accuracy, when two thirds of the geometric inputs are outside the training range of that model. Finally, the individual components of the potential energy are analyzed, and chemical intuition is reflected in the independent behavior of the three energy terms [Formula: see text](intra-atomic), [Formula: see text] (electrostatic) and [Formula: see text] (exchange), in contrast to standard force fields.

SUBMITTER: Zielinski F

PROVIDER: S-EPMC5634454 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Geometry Optimization with Machine Trained Topological Atoms.

Zielinski François F Maxwell Peter I PI Fletcher Timothy L TL Davie Stuart J SJ Di Pasquale Nicodemo N Cardamone Salvatore S Mills Matthew J L MJL Popelier Paul L A PLA

Scientific reports 20171009 1

The geometry optimization of a water molecule with a novel type of energy function called FFLUX is presented, which bypasses the traditional bonded potentials. Instead, topologically-partitioned atomic energies are trained by the machine learning method kriging to predict their IQA atomic energies for a previously unseen molecular geometry. Proof-of-concept that FFLUX's architecture is suitable for geometry optimization is rigorously demonstrated. It is found that accurate kriging models can opt ...[more]

PMID: 28993674

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Project description:Background: Optical microtraps at the focus of high numerical aperture (high-NA) imaging systems enable efficient collection, trapping, detection and manipulation of individual neutral atoms for quantum technology and studies of optical physics associated with super- and sub-radiant states. The recently developed "Maltese cross" geometry (MCG) atom trap uses four in-vacuum lenses to achieve four-directional high-NA optical coupling to single trapped atoms and small atomic arrays. This article presents the first extensive characterisation of atomic behaviour in a MCG atom trap. Methods: We employ a MCG system optimised for high coupling efficiency and characterise the resulting properties of the trap and trapped atoms. Using current best practices, we measure occupancy, loading rate, lifetime, temperature, fluorescence anti-bunching and trap frequencies. We also use the four-directional access to implement a new method to map the spatial distribution of collection efficiency from high-NA optics: we use the two on-trap-axis lenses to produce a 1D optical lattice, the sites of which are stochastically filled and emptied by the trap loading process. The two off-trap-axis lenses are used for imaging and single-mode collection. Correlations of single-mode and imaging fluorescence signals are then used to map the single-mode collection efficiency. Results: We observe trap characteristics comparable to what has been reported for single-atom traps with one- or two-lens optical systems. The collection efficiency distribution in the axial and transverse directions is directly observed to be in agreement with expected collection efficiency distribution from Gaussian beam optics. Conclusions: The multi-directional high-NA access provided by the Maltese cross geometry enables complex manipulations and measurements not possible in geometries with fewer directions of access, and can be achieved while preserving other trap characteristics such as lifetime, temperature, and trap size.

Dataset Information

Geometry Optimization with Machine Trained Topological Atoms.

Publications

Geometry Optimization with Machine Trained Topological Atoms.

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