  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.
