Overview
Platform Motivation
The widespread adoption of stretchable nanocomposites faces several key challenges, including (1) the lack of comprehensive, high-quality experimental datasets, (2) inefficient dissemination mechanisms that limit collaboration among diverse stakeholders, and (3) absence of accessible data platforms and user-friendly visualization tools. To address these persistent challenges, we have established a data-sharing platform that compiles approximately 150k feasible fabrication parameters of G₁/G₂ stretchable nanocomposites, along with their machine learning (ML)-predicted properties (S₀ and ε10% (%)) and prediction uncertainties. This platform is part of a research project led by Dr. Po-Yen Chen's group at the University of Maryland, College Park.
Platform Description
This data-sharing platform features two key functionalities forward prediction and inverse design.
In the forward prediction tab, users can select a set of composition and fabrication parameters from Section I and II, respectively. The platform then uses its embedded prediction models to forecast the feasibility of filtered nanocomposite, S₀, ε10% (%), and prediction uncertainties from the selected parameters.
In the inverse design, users can specify target property requirements, prompting the platform to perform cluster analyses using the embedded ML-enabled prediction model. The platform then recommends the most suitable composition and fabrication parameters, enabling users to interactively optimize stretchable nanocomposites.