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We introduce a method for elucidating and modifying the functionality ofsystems dominated by rare events that relies on the automated tuning of theirunderlying free energy surface. The proposed approach seeks to constructcollective variables (CVs) that encode the essential information regarding therare events of the system of interest. The appropriate CVs are identified usingHarmonic Linear Discriminant Analysis (HLDA), a machine-learning based methodthat is trained solely on data collected from short ordinary simulations in therelevant metastable states of the system. Utilizing the interpretable form ofthe resulting CVs, the critical interaction potentials that determine thesystem's rare transitions are identified and purposely modified to tailor thefree energy surface in manner that alters functionality as desired. Theapplicability of the method is illustrated in the context of three differentsystems, thereby demonstrating that thermodynamic and kinetic properties can betractably modified with little to no prior knowledge or intuition.

Collective Variables for Free Energy Surface Tailoring -- Understanding and Modifying Functionality in Systems Dominated by Rare Events