Neural network approximates potential from Hamiltonian trajectories then equation discovery extracts algebraic expression matching ground truth on oscillators, central force, and Coulomb problems.
The Journal of Chemical Physics 145(17), 170901 (2016)
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Learning and Interpreting Potentials for Classical Hamiltonian Systems
Neural network approximates potential from Hamiltonian trajectories then equation discovery extracts algebraic expression matching ground truth on oscillators, central force, and Coulomb problems.
- A density-functional perspective on force fields