Research · 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. Classification of different types of symmetry relevant to machine learning computational techniques. 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.