Dataset Information

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:Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional-step-restricted second-order truncated expansion-molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In this paper, we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect, the GEK procedure will be used to mimic the behavior of a conventional second-order scheme but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimizations to facilitate restricted-variance optimizations. A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two: the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, full Baker-TS, and S22 test suites, at the density functional theory and second-order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected.

Dataset Information

Geometry Optimization with Machine Trained Topological Atoms.

Publications

Geometry Optimization with Machine Trained Topological Atoms.

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