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外军国防科技报告
>ARL-TR-8354 - Machine Learning Intermolecular Potentials for 1,3,5-Triamino-2,4,6-trinitrobenzene (TATB) Using Symmetry-Adapted Perturbation Theory | U.S. Army Research Laboratory
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ARL-TR-8354 - Machine Learning Intermolecular Potentials for 1,3,5-Triamino-2,4,6-trinitrobenzene (TATB) Using Symmetry-Adapted Perturbation Theory | U.S. Army Research Laboratory
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 learningpotentials and that machine learning methods can accurately describe noncovalent interactions in energetic materials.
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