Manifold Learning in Atomistic Simulations: A Conceptual Review

Understand

Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations.

• Such practice is needed in atomistic simulations of complex systems where even thousands of degrees of freedom are sampled.
• An abundance of such data makes gaining insight into a specific physical problem strenuous.
• Our primary aim in this review is to focus on unsupervised machine learning methods that can be used on simulation data to find a low-dimensional manifold providing a collective and informative characterization of the studied process.