31 citations · 110 across the 10 of their papers we have counts for
5 papers · 1 filter
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Tsz Wai Ko, Bowen Deng, Marcel Nassar +7
Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the…
Foundational Large Language Models for Materials Research
Vaibhav Mishra, Somaditya Singh, Dhruv Ahlawat +7
Materials discovery and development are critical for addressing global challenges. Yet, the exponential growth in materials science literature comprising vast amounts of textual da…
From Text to Insight: Large Language Models for Materials Science Data Extraction
Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih +5
The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditional…
Less can be more for predicting properties with large language models
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka
Predicting properties from coordinate-category data -- sets of vectors paired with categorical information -- is fundamental to computational science. In materials science, this ch…
Are LLMs Ready for Real-World Materials Discovery?
Santiago Miret, N M Anoop Krishnan
Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science. While LLMs have great potential to ac…