Molecular conformer search with low-energy latent space

TL;DR

This paper introduces a novel method using a variational auto-encoder to perform molecular conformer searches in a low-energy latent space, improving efficiency and accuracy in identifying low-energy molecular configurations.

Contribution

The work presents a low-energy latent-space (LOLS) approach that biases a VAE towards low-energy conformers, enabling effective conformer search and energy modeling for molecules with multiple degrees of freedom.

Findings

Method successfully identifies low-energy conformers in benchmark tests.

Results align with previous studies, validating the approach.

Efficient conformer search in molecules with 5-9 dimensions.

Abstract

Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data. In this way, we can effectively build a reliable energy model for the low-energy potential energy surface. After the energy model has been built, we extract local-minimum conformations and refine them with structure optimization. We have tested and benchmarked our low-energy latent-space (LOLS) structure search method on organic molecules with $5-9$ searching dimensions. Our results agree with previous studies.