Graph Learning Metallic Glass Discovery from Wikipedia

TL;DR

This paper introduces a novel AI-driven approach using graph neural networks and Wikipedia-based language models to accelerate the discovery of metallic glasses, overcoming data scarcity and encoding challenges.

Contribution

It presents a new paradigm leveraging Wikipedia embeddings and graph neural networks for materials discovery, enhancing predictability and exploration in material design.

Findings

Wikipedia embeddings improve material encoding.

Graph neural networks effectively identify hidden material relationships.

Multilingual embeddings enhance discovery potential.

Abstract

Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal combinations of multiple elements to resist crystallization. This constraint renders only several thousands of candidates explored in the vast material space since 1960. Recently, data-driven approaches armed by advanced machine learning techniques provided alternative routes for intelligent materials design. Due to data scarcity and immature material encoding, the conventional tabular data is usually mined by statistical learning algorithms, giving limited model predictability and generalizability. Here, we propose sophisticated data learning from material network representations. The node elements are encoded from the Wikipedia by a language model. Graph neural networks with versatile architectures are designed to serve as recommendation systems to explore hidden relationships among materials. By employing Wikipedia embeddings from different languages, we assess the capability of natural languages in materials design. Our study proposes a new paradigm to harvesting new amorphous materials and beyond with artificial intelligence.