# Automatic extraction of materials and properties from superconductors scientific literature

arXiv: https://arxiv.org/abs/2210.15600 · HTML: https://mlsanity.com/paper/2210.15600/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [Head-Driven Phrase Structure Grammar Parsing on Penn Treebank](https://mlsanity.com/paper/1907.02684/) (2019)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://mlsanity.com/paper/1706.09528/) (2017)
- [Machine learning modeling of superconducting critical temperature](https://mlsanity.com/paper/1709.02727/) (2017)
- [A* CCG Parsing with a Supertag and Dependency Factored Model](https://mlsanity.com/paper/1704.06936/) (2017)
- [pyBART: Evidence-based Syntactic Transformations for IE](https://mlsanity.com/paper/2005.01306/) (2020)
- [Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach](https://mlsanity.com/paper/2002.04977/) (2020)
- [Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB2 Near Hydrogen Liquefaction Temperature](https://mlsanity.com/paper/2005.05618/) (2020)

## Built on by (most cited first, 6 of 6)

- [Mining experimental data from Materials Science literature with Large Language Models: an evaluation study](https://mlsanity.com/paper/2401.11052/) (2024)
- [Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function](https://mlsanity.com/paper/2401.16611/) (2024)
- [ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature](https://mlsanity.com/paper/2510.20362/) (2025)
- [Semi-automatic staging area for high-quality structured data extraction from scientific literature](https://mlsanity.com/paper/2309.10923/) (2023)
- [Construction of a Battery Research Knowledge Graph using a Global Open Catalog](https://mlsanity.com/paper/2604.20241/) (2026)
- [Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion](https://mlsanity.com/paper/2609.26381/) (2026)

## Abstract (arXiv)

The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics). In this paper, we discuss Grobid-superconductors, our solution for automatically extracting superconductor material names and respective properties from text. Built as a Grobid module, it combines machine learning and heuristic approaches in a multi-step architecture that supports input data as raw text or PDF documents. Using Grobid-superconductors, we built SuperCon2, a database of 40324 materials and properties records from 37700 papers. The material (or sample) information is represented by name, chemical formula, and material class, and is characterized by shape, doping, substitution variables for components, and substrate as adjoined information. The properties include the Tc superconducting critical temperature and, when available, applied pressure with the Tc measurement method.