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

TL;DR

This paper introduces a physics-informed machine learning approach to systematically modify the functionality of disordered elastic networks by tailoring their free energy surfaces, enhancing design capabilities at molecular scales.

Contribution

It combines AI with physics-based collective variables trained on single-system data to identify key interactions and engineer system functionalities.

Findings

Successfully engineered allosteric regulation.

Controlled uniaxial strain fluctuations.

Demonstrated potential for complex molecular system design.

Abstract

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