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Computing accurate reaction rates is a central challenge in computationalchemistry and biology because of the high cost of free energy estimation withunbiased molecular dynamics. In this work, a data-driven machine learningalgorithm is devised to learn collective variables with a multitask neuralnetwork, where a common upstream part reduces the high dimensionality of atomicconfigurations to a low dimensional latent space, and separate downstream partsmap the latent space to predictions of basin class labels and potentialenergies. The resulting latent space is shown to be an effectivelow-dimensional representation, capturing the reaction progress and guidingeffective umbrella sampling to obtain accurate free energy landscapes. Thisapproach is successfully applied to model systems including a 5D M\"uller Brownmodel, a 5D three-well model, and alanine dipeptide in vacuum. This approachenables automated dimensionality reduction for energy controlled reactions incomplex systems, offers a unified framework that can be trained with limiteddata, and outperforms single-task learning approaches, including autoencoders.

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