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Bond sensitive graph neural networks for predicting high temperature superconductors
Bond sensitive graph neural networks for predicting high temperature superconductors
- Source :
- Materials Genome Engineering Advances, Vol 2, Iss 2, Pp n/a-n/a (2024)
- Publication Year :
- 2024
- Publisher :
- Wiley-VCH, 2024.
-
Abstract
- Abstract Finding high temperature superconductors (HTS) has been a continuing challenge due to the difficulty in predicting the transition temperature (Tc) of superconductors. Recently, the efficiency of predicting Tc has been greatly improved via machine learning (ML). Unfortunately, prevailing ML models have not shown adequate generalization ability to find new HTS, yet. In this work, a graph neural network model is trained to predict the maximal Tc (Tcmax) of various materials. Our model reveals a close connection between Tcmax and chemical bonds. It suggests that shorter bond lengths are favored by high Tc, which is in coherence with previous domain knowledge. More importantly, it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high Tc, which is new even to the human experts. It can provide a convenient guidance to the materials scientists in search of HTS.
Details
- Language :
- English
- ISSN :
- 29409497 and 29409489
- Volume :
- 2
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- Materials Genome Engineering Advances
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.9de4da3fcf99408e855f255c1e7ab0c5
- Document Type :
- article
- Full Text :
- https://doi.org/10.1002/mgea.48