Latent Sequence Generation of Steered Molecular Dynamics

In this paper, we use a LSTM-VAE model framework in order to learn latent representations that are conditioned by potential energy through TorchMD, while being able to autoregressively generate sequences of a 10 deca-alanine system. While previous work have used generative deep learning methods for learning latent representations and predicting motion of molecules, this paper tackles with the latent representations for steered molecular dynamics (SMD).