In recent years, in the field of education, there has been a clear progressive trend toward
precision education. As a rapidly evolving AI technique, machine learning is viewed as an …

Conventional machine learning approaches for predicting material properties from
elemental compositions have emphasized the importance of leveraging domain knowledge …

Abstract Machine learning has emerged as a novel tool for the efficient prediction of material
properties, and claims have been made that machine-learned models for the formation …

The empirical rules for the prediction of solid solution formation proposed so far in the
literature usually have very compromised predictability. Some rules with seemingly good …

Significant attempts have been made to improve the production of ion-selective membranes
(ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks …

Abstract Machine learning (ML) models for predicting gas permeability through polymers
have traditionally relied on experimental data. While these models exhibit robustness within …

Modern data-driven tools are transforming application-specific polymer development cycles.
Surrogate models that can be trained to predict properties of polymers are becoming …

Guiding experiments to find materials with targeted properties is a crucial aspect of materials
discovery and design, and typically multiple properties, which often compete, are involved …

Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great
interest. However, it is difficult to find a universal expression to predict the elastic moduli …

Many technological applications depend on the response of materials to electric fields, but
available databases of such responses are limited. Here, we explore the infrared …