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Advances in manufacturing and characterization of complex molecular systemshave created a need for new methods for design at molecular length scales.Emerging approaches are increasingly relying on the use of ArtificialIntelligence (AI), and the training of AI models on large data libraries. Thisparadigm shift has led to successful applications, but shortcomings related tointerpretability and generalizability continue to pose challenges. Here, weexplore an alternative paradigm in which AI is combined with physics-basedconsiderations for molecular and materials engineering. Specifically,collective variables, akin to those used in enhanced sampled simulations, areconstructed using a machine learning (ML) model trained on data gathered from asingle system. Through the ML-constructed collective variables, it becomespossible to identify critical molecular interactions in the system of interest,the modulation of which enables a systematic tailoring of the system's freeenergy landscape. To explore the efficacy of the proposed approach, we use itto engineer allosteric regulation, and uniaxial strain fluctuations in acomplex disordered elastic network. Its successful application in these twocases provides insights regarding how functionality is governed in systemscharacterized by extensive connectivity, and points to its potential for designof complex molecular systems.

Systematic Modification of Functionality in Disordered Elastic Networks Through Free Energy Surface Tailoring