Machine Learning Models for Predicting Polymer Solubility in Solvents across Concentrations and Temperatures.

Artificial intelligence and machine learning have become essential tools in predicting material properties to aid in the accelerated design of new materials. Polymer solubility, critical for new formulations and solution processing, is one such property. However, current models are limited by inadequate experimental data sets that cannot capture the complexity and detail for many features contributing to polymer solubility.

Design of Recyclable Plastics with Machine Learning and Genetic Algorithm.

We present an artificial intelligence-guided approach to design durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs a genetic algorithm (GA) that designs new monomers and then utilizes virtual forward synthesis (VFS) to generate almost a million ROP polymers. Machine learning models to predict thermal, thermodynamic, and mechanical properties─crucial for application-specific performance and recyclability─are used to guide the GA toward optimal polymers.

Fast and Efficient Fabrication of Functional Electronic Devices through Grayscale Digital Light Processing 3D Printing.

Fabricating polymeric composites with desirable characteristics for electronic applications is a complex and costly process. Digital light processing (DLP) 3D printing emerges as a promising technique for manufacturing intricate structures. In this study, polymeric samples are fabricated with a conductivity difference exceeding three orders of magnitude in various portions of a part by employing grayscale DLP (g-DLP) single-vat single-cure 3D printing deliberate resin design.