Tech · NIST AI
Machine Learning to Predict Multicomponent Colloidal Crystals
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The team use machine learning and other computational techniques to predict the self-assembly characteristics of multicomponent colloidal materials.
Key facts
- This will explore the application of powerful, modern ML approaches to achieve inverse design more accurately, in less time, and in an experimentally viable fashion
- PACCS code for a colloidal crystal structure analysis, generation, optimization, and visualization library written in Python is freely available on gihub
- The team use machine learning and other computational techniques to predict the self-assembly characteristics of multicomponent colloidal materials
- In general, colloids are facile material building blocks whose shape, charge, and surface functionalization can be tuned to control their assembly
Summary
There is a direct link between a material’s macroscopic properties and its microscopic structure, which makes rational bottom-up self-assembly a powerful tool for engineering properties of materials. In general, colloids are facile material building blocks whose shape, charge, and surface functionalization can be tuned to control their assembly. The team have recently developed an approach for systematically enumerating crystalline ground states of two-dimensional multicomponent colloidal materials using symmetry. This allows them to generate phase diagrams for multicomponent mixtures which govern their equilibrium self-assembly, by searching over many, if not nearly all, relevant competing crystalline lattices. This project will use theory, simulation, and machine learning to understand how chemical functionalization of soft matter systems ( e.g., colloids, polymers) affects their self-assembly into different crystals or other morphologies, and how this is related to their equilibrium phase behavior.