Data Driven characterisation of the Free Energy Landscape of Syndiotatic Polysterene

Syndiotatic polysterene (sPS) exhibits complex polymorphic behaviour, resulting in rugged free energy landscapes (FELs) with high energy barriers. Enhanced sampling methods have the potential remedy to overcome the barriers with prior knowledge of collective variables (CVs), typically identified through physical or chemical expertise. Autoencoders are powerful tools for providing a low-dimensional embedding of the essential features, since the technique forces an information compression in the bottleneck region. A specialised autoencoder architecture, the Gaussian mixture variational autoencoder (GMVAE), performs dimensionality reduction and clustering within a single unified framework, and can identify the inherent dimensionality of the system by enforcing physical constraints in the latent space. In order to efficiently describe the local environment of sPS monomers, we adapt an atomic representation used in machine learning. One of the advantages of using these descriptors is that they do not require incorporation of excessive system-specific intuition and demonstrate good transferability properties. With this data-driven approach, we aim to characterise the pathways between polymorphic transitions.