An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors.

ABSTRACT: Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry Functions (ACSFs) offer an intuitive featurization approach by means of a tedious and labor-intensive selection of tunable parameters. In this work, we implement an unsupervised ML strategy relying on a Gaussian Mixture Model (GMM) to automatically optimize the ACSF parameters. GMMs effortlessly decompose the vastness of the chemical and conformational spaces into well-defined radial and angular clusters, which are then used to build tailor-made ACSFs. The unsupervised exploration of the space has demonstrated general applicability across a diverse range of systems, spanning from various unimolec

SUBMITTER: Gallegos M 

PROVIDER: S-EPMC11040729 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature