U.S. · NIST AI
Machine Learning to Predict Multicomponent Colloidal Crystals
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Key facts
- Official websites use.gov A.gov website belongs to an official government organization in the United States
- Secure.gov websites use HTTPS A lock or https:// means you’ve safely connected to the.gov website — 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
Summary
Secure.gov websites use HTTPS A lock or https:// means you’ve safely connected to the.gov website. The team use machine learning and other computational techniques to predict the self-assembly characteristics of multicomponent colloidal materials. 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.