Advances in materials science require leveraging past findings and data from the vast
published literature. While some materials data repositories are being built, they typically …

The application of supervised Machine Learning (ML) in material science, especially
towards the design of structural Multi-Principal Element Alloys (MPEAs) has rapidly …

The discovery of novel magnetic materials with greater operating temperature ranges and
optimized performance is essential for advanced applications. Current data-driven …

A comprehensive database of magnetic materials is valuable for researching the properties
of magnetic materials and discovering new ones. This article introduces a novel workflow …

In this study, we develop and release two Bidirectional Encoder Representations (BERT)
models that are trained primarily with roughly≈ 144 K peer-reviewed publications within the …

Vast published dielectric ceramics literature is a natural database for big-data analysis,
discovering structure–property relationships, and property prediction. We constructed a data …

Automated materials synthesis requires historical data, but extracting detailed data and
metadata from publications is challenging. We developed initial strategies for using large …

In China, fruit tree diseases are a significant threat to the development of the fruit tree
industry, and knowledge about fruit tree diseases is the most needed professional …

Vector embeddings derived from large language models (LLMs) show promise in capturing
latent information from the literature. Interestingly, these can be integrated into material …

Advances in materials science depend on leveraging data from the vast published literature.
Extracting detailed data and metadata from these publications is challenging, leading …