Platform Motivation
The widespread adoption of sustainable biobased nanocomposites faces several key challenges, including (1) the lack of comprehensive, high-quality experimental datasets, (2) inefficient dissemination mechanisms that limits collaboration among diverse stakeholders, and (3) absence of accessible data platforms and user-friendly visualization tools. To address these barriers, we decided to establish a data-sharing platform that has compiled approximately 1 billion formulations of biobased nanocomposites, along with their model-predicted properties.
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 any five components at varying ratios, and the platform will utilize its embedded prediction model to generate property predictions for biobased nanocomposites.
In the inverse design tab, users can define targeted property requirements, prompting the platform to perform cluster analyses using the embedded prediction model. The platform will suggest most suitable formulations, allowing users to interactively optimize formulations within the recommended loading range for each selected component.
In the forward prediction tab, users can select a set of any five components at varying ratios, and the platform will utilize its embedded prediction model to generate property predictions for biobased nanocomposites.
In the inverse design tab, users can define targeted property requirements, prompting the platform to perform cluster analyses using the embedded prediction model. The platform will suggest most suitable formulations, allowing users to interactively optimize formulations within the recommended loading range for each selected component.
-Hayden Whitley, Tianle Chen, Dr. Yang Li, Dr. Po-Yen Chen