Post accepted to the ICLR 2022 Blog Track. In this post, we provide an in-depth overview of methods outlined in the paper “Learning Neural Generative Dynamics for Molecular Conformation Generation,” discuss the impact of the work in the context of other conformation generation approaches, and additionally discuss future potential applications to improve the diversity and stability of generated conformations.

We interact with machine learning algorithms every day–from scrolling through social media, to navigating around town, to selecting word recommendations when we text. But what do we know about these machine learning methods?

Richard Feynman once said “What I cannot create I do not understand.” Therefore, to truly understand the human visual system, we must learn to create it. One of the most effective forms of such creation is the Convolutional Neural Network (CNN) system to mimic the human visual system.