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Found 6 papers, 3 papers with code
Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction
Discovery of new materials has a documented history of propelling human progress for centuries and more.
MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models
Further, we evaluate the performance of GPT-3. 5 and GPT-4 models on solving these questions via zero-shot and chain of thought prompting.
Cementron: Machine Learning the Constituent Phases in Cement Clinker from Optical Images
Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron.
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
A crucial component in the curation of KB for a scientific domain (e. g., materials science, foods & nutrition, fuels) is information extraction from tables in the domain's published research articles.
MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction
Here, we present a materials-aware language model, namely, MatSciBERT, which is trained on a large corpus of scientific literature published in the materials domain.
Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing
Most of the knowledge in materials science literature is in the form of unstructured data such as text and images.
Image Comprehension
Digital Libraries
Computational Physics
Data Analysis, Statistics and Probability
