#materials informatics

AI (Artificial Intelligence) plays a transformative role in materials science and engineering, offering a wide range of capabilities that enhance research, development, and application of materials.

AI intervenes in materials discovery and design, high-throughput screening and experimentation, materials characterization and analysis, materials modeling and simulation, materials performance optimization, materials lifecycle and sustainability, and collaborative platforms and knowledge sharing.

Read more:

Automated simulation software creates a world map of polymer properties

A research team has published their method to create a comprehensive database of polymer properties, as well as experimental validation, in npj Computational Materials.

"Materials informatics (MI), a new branch of materials research that combines materials data with data science, is gaining traction," said co-corresponding author Yoshihiro Hayashi, assistant professor, Institute of Statistical Mathematics in the Research Organization of Information and Science (ROIS). Hayashi is also affiliated with the University of Tokyo's Department of Mechanical Engineering. "MI applies machine learning to predict new materials with innovative properties and their fabrication methods from a vast design space. As such, data is the most important resource in MI."

Despite the need, Hayashi said, efforts to create a comprehensive database of polymer properties to enable data-driven research have fallen short.

"To construct a database of polymer properties by molecular simulations, we developed RadonPy," Hayashi said. "It's the first open-source software that successfully automates polymer physical property calculations using simulations of classical molecular dynamics based on atomistic models—which account for the behaviors and characteristics of individual constituents."