Machine Learning Intermolecular Potentials for 1,3,5-Triamino-2,4,6-trinitrobenzene (TATB) Using Symmetry-Adapted Perturbation Theory

Abstract

In this report, intermolecular potentials for 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) are developed using machine learning techniques. Three potentials based on support vector regression, kernel ridge regression, and a neural network are fit using symmetry-adapted perturbation theory. The potentials are used to explore minima on the TATB dimer potential energy surface. It is demonstrated that the ab initio potential energy surface is accurately characterized by the machine learning potentials and that machine learning methods can accurately describe noncovalent interactions in energetic materials.

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Document Details

Document Type
Technical Report
Publication Date
Apr 25, 2018
Accession Number
AD1050897

Entities

People

  • Decarlos E. Taylor

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Artificial Neural Networks
  • Computational Chemistry
  • Computational Science
  • Computer Languages
  • Deep Learning
  • Density Functional Theory
  • Energetic Materials
  • Energy
  • Insensitive Explosives
  • Learning
  • Machine Learning
  • Materials
  • Military Research
  • Molecular Dynamics
  • Neural Networks
  • Perturbation Theory
  • Perturbations
  • Potential Energy
  • Public Health
  • Quantum Theory
  • Supervised Machine Learning
  • Symmetry
  • Test Sets

Fields of Study

  • Chemistry

Readers

  • Neural Network Machine Learning.
  • Quantum Chemistry
  • Rocket Propulsion.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks