Automatic extraction of materials and properties from superconductors scientific literature

Abstract

In this study, we propose a staging area for ingesting new superconductors' experimental data in SuperCon that is machine-collected from scientific articles. Our objective is to enhance the efficiency of updating SuperCon while maintaining or enhancing the data quality. We present a semi-automatic staging area driven by a workflow combining automatic and manual processes on the extracted database. An anomaly detection automatic process aims to pre-screen the collected data. Users can then manually correct any errors through a user interface tailored to simplify the data verification on the original PDF documents. Additionally, when a record is corrected, its raw data is collected and utilised to improve machine learning models as training data. Evaluation experiments demonstrate that our staging area significantly improves curation quality. We compare the interface with the traditional manual approach of reading PDF documents and recording information in an Excel. Using the interface boosts the precision and recall by 6% and 50%, respectively to an average increase of 40% in F1-score.

Discovering Chemically Novel, High-Temperature Superconductors

“…There have been various applications in this work each with promising discoveries of new superconductive materials and properties with most of them predicting critical temperature. For example, superconducting phase diagrams were predicted using text mining [8], superconducting hydrogen compounds were found using a genetic algorithm and genetic programming [9] critical temperature and pressure were predicted for hydrides [10], critical temperatures of doped Fe-based superconductors were predicted based on structural and topological parameters [11], and critical temperature was predicted on a structure based model using a structural descriptor [12], and superconductor materials and properties have been automatically extracted from literature [13]. An ML-guided discovery will hopefully replace the "serendipitous discovery paradigm" that has existed in this last century of superconductor research [14].…”

Discovering Chemically Novel, High-Temperature Superconductors

“…A useful tool to accomplish this goal of accelerated superconductive materials discovery is through machine learning, where there has been various implementations. For example, superconducting phase diagrams were predicted using text mining [9], superconducting hydrogen compounds were found using a genetic algorithm and genetic programming [10], critical temperature and pressure were predicted for hydrides [11], critical temperatures of doped Fe-based superconductors were predicted based on structural and topological parameters [12], and critical temperature was predicted on a structure based model using a structural descriptor [13], and superconductor materials and properties have been automatically extracted from literature [14]. An ML-guided discovery will hopefully replace the "serendipitous discovery paradigm" that has existed in this last century of superconductor research [15].…”