Multitask machine learning of collective variables for enhanced sampling of rare events

TL;DR

This paper introduces a multitask neural network approach to learn collective variables for enhanced sampling in molecular dynamics, improving free energy landscape estimation in complex systems.

Contribution

It presents a novel multitask machine learning framework that reduces dimensionality and predicts reaction progress and energies simultaneously, outperforming existing single-task methods.

Findings

Effective low-dimensional latent space captures reaction progress.

Guides umbrella sampling for accurate free energy landscapes.

Outperforms autoencoders and single-task models.

Abstract

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space, and separate downstream parts map the latent space to predictions of basin class labels and potential energies. The resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D M\"uller Brown model, a 5D three-well model, and alanine dipeptide in vacuum. This approach enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.