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Found 6 papers, 3 papers with code
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge amount of unlabelled material data.
Population Analysis with Wannier Orbitals
We formulate Wannier orbital overlap population and Wannier orbital Hamilton population to describe the contribution of different orbitals to electron distribution and their interactions.
Computational Physics Materials Science
CrysMMNet: Multimodal Representation for Crystal Property Prediction
In this work, we leverage textual descriptions of materials to model global structural information into graph structure and learn a more robust and enriched representation of crystalline materials.
MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature
The number of published articles in the field of materials science is growing rapidly every year.
Using Sentence-level Classification Helps Entity Extraction from Material Science Literature
In the last few years, several attempts have been made on extracting information from material science research domain.
MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction
Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc.
