jakublala / md-neural-ode

First two problems of my Master's Thesis on Coarse-Graining of Molecular Dynamics with Neural Ordinary Differential Equations
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deep-learning machine-learning molecular-dynamics neural-ode toy-problems

Neural ODEs: Toy Potentials and Triatomic Harmonic Molecule

This is a repository for the first two problems of my Master's Thesis called Coarse-Graining of Molecular Dynamics Using Neural Ordinary Differential Equations. The project has two parts. Firstly, three different toy potentials are learned with neural ODEs. Then a benchmark is done by performing Hamiltonian Monte Carlo sampling of those potentials, where the dynamics proposal step is evolved with neural ODEs. Secondly, a diatomic harmonic molecule potential is learned and then extended to a triatomic molecule, investigating the feasibility of extending pair-wise interactions.

This project was done as a collaboration between MIT's Rafael Gómez-Bombarelli and Imperial's Stefano Angiolleti-Uberti research groups.

Toy Potential Results

2D Shell

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10D Gaussian

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2D Wolfe-Quapp

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Diatomic & Triatomic Harmonic Molecule Results

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