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A variety of enhanced sampling methods predict multidimensional free energylandscapes associated with biological and other molecular processes as afunction of a few selected collective variables (CVs). The accuracy of thesemethods is crucially dependent on the ability of the chosen CVs to capture therelevant slow degrees of freedom of the system. For complex processes, findingsuch CVs is the real challenge. Machine learning (ML) CVs offer, in principle,a solution to handle this problem. However, these methods rely on theavailability of high-quality datasets -- ideally incorporating informationabout physical pathways and transition states -- which are difficult to access,therefore greatly limiting their domain of application. Here, we demonstratehow these datasets can be generated by means of enhanced sampling simulationsin trajectory space via the metadynamics of paths [arXiv:2002.09281] algorithm.The approach is expected to provide a general and efficient way to generateefficient high quality d CVs for the fast prediction of free energy landscapes.We demonstrate our approach on two numerical examples, a two-dimensional modelpotential and the isomerization of alanine dipeptide, using deep targeteddiscriminant analysis as our ML-based CV of choice.

Effective Data-Driven Collective Variables for Free Energy Calculations from Metadynamics of Paths