Issue 4, 2022

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Abstract

Polarizable nanoparticles are of interest in materials science because of their rich and complex phase behavior that can be used to engineer nanostructured materials with long-range crystalline order. To understand and rationally navigate the design space of polarizable nanoparticles for self-assembling highly ordered superlattices, we developed a coarse-grained computational model to describe the nanoparticle–nanoparticle interactions in implicit solvent and employ the computationally efficient image method to model many-body polarization interactions. We conducted high-throughput virtual screening over a five-dimensional particle design space spanned by temperature, particle size, particle charge, particle dielectric, and solvent dielectric using enhanced sampling molecular dynamics calculations within an active learning framework to efficiently map out the regions of thermodynamic stability of the self-assembled aggregates. We validate our predictions in comparisons against small angle X-ray scattering measurements of gold nanoparticles surface functionalized with metal chalcogenide complex ligands. Finally, we use our validated phase maps to computationally design switchable nanostructured materials capable of triggered assembly and disassembly as a function of temperature and solvent dielectric with potential applications as sensors, smart windows, optoelectronic devices, and in medical diagnostics.

Graphical abstract: Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Supplementary files

Article information

Article type
Paper
Submitted
13 Dis 2021
Accepted
24 Jan 2022
First published
24 Jan 2022

Mol. Syst. Des. Eng., 2022,7, 350-363

Author version available

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

S. Dasetty, I. Coropceanu, J. Portner, J. Li, J. J. de Pablo, D. Talapin and A. L. Ferguson, Mol. Syst. Des. Eng., 2022, 7, 350 DOI: 10.1039/D1ME00187F

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