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Neural Network Models of Potential Energy Surfaces: Prototypical Examples
James B Witkoskie1, Douglas J Doren1
1Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716.
Journal of Chemical Theory and Computation
|December 8, 2015
Summary
Neural networks accurately model potential energy hypersurfaces using ab initio data. Optimal fitting, enhanced by gradients, provides reliable interpolations even with noisy data.
Area of Science:
- Computational chemistry
- Machine learning in materials science
Background:
- Neural networks can approximate potential energy hypersurfaces from discrete ab initio data.
- Prior studies demonstrate accuracy in complex, high-dimensional systems.
Purpose of the Study:
- Investigate fundamental properties of neural network potentials in 1D, 2D, and 3D prototypes.
- Understand data requirements, network configurations, and neuron counts for accurate fits.
Main Methods:
- Utilized an extended Kalman filtering algorithm for optimal parameter adjustment.
- Explored fitting with energy data and combined energy and gradient data.
Main Results:
- Identified relationships between function complexity, data volume, and optimal network architecture.
- Demonstrated that including gradient information significantly improves interpolation accuracy.
- Showcased the robustness of the fitting algorithm against random noise in the data.
Conclusions:
- Neural network potentials offer a viable method for representing potential energy hypersurfaces.
- The extended Kalman filter provides an effective approach for optimizing network parameters.
- Availability of gradient data is crucial for high-fidelity interpolations in computational chemistry.
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