Machine Learning Guided Multiscale Design of DNA-functionalized Nanoparticles for Targeted Self-Assembly of the Double Gyroid
Luis Nieves-Rosado (1), Fernando Escobedo (1) ((1) Cornell University)

TL;DR
This study develops a machine learning-guided multiscale approach to design DNA-functionalized nanoparticles that self-assemble into a double gyroid structure, enabling targeted material design in soft matter science.
Contribution
It introduces a novel active learning framework combining free energy calculations and coarse-grained modeling for designing self-assembling nanoparticles.
Findings
Identified specific nanoparticle designs that form the double gyroid phase.
Validated designs through interfacial pinning calculations.
Demonstrated the method's extensibility to other structures.
Abstract
In soft matter science, it is often the goal to design new materials with targeted properties. These materials can be used in many applications, each requiring specific features to be optimized for maximum fitness. The use of self-assembly processes, where a target structure is attained from suitably designed building blocks, is a powerful tool for this optimization. However, it is still an open question how to generally design realizable building blocks that lead to the desired phases. In this work, the double gyroid is chosen as target structure and DNA functionalized nanoparticles are used as our building blocks. Using existing pair potentials as inspiration, a large design space is defined for exploration of the target structure. An effective search strategy is implemented, where free energy calculations are first used to coarse grain our originally fine-grained model of the…
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