SEHC: A high-throughput materials computing framework with automatic self-evaluation filtering

•A self-evaluation high-throughput computing framework is proposed.•The framework relies on machine learning methods to screening expected materials.•The framework dramatically improves the high-throughput computing efficiency.•The high-throughput computing stage should be standardized. Efficiency i...

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Bibliographic Details
Published in:Materials science & engineering. B, Solid-state materials for advanced technology Solid-state materials for advanced technology, 2020-02, Vol.252, p.1-16, Article 114474
Main Authors: Zhu, Wenhao, Xu, Yonglin, Ni, Jianyue, Hu, Guannan, Wang, Xiangmeng, Zhang, Wu
Format: Article
Language:English
Subjects:
Data storage
Design specifications
Design standards
Diamond-like structures
Diamonds
Filtration
Group velocity
High-throughput
High-throughput Stage
Machine learning
Prototypes
SEHC
Self evaluation
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Summary:•A self-evaluation high-throughput computing framework is proposed.•The framework relies on machine learning methods to screening expected materials.•The framework dramatically improves the high-throughput computing efficiency.•The high-throughput computing stage should be standardized. Efficiency is one of the key problems in the design of high-throughput materials computing. In this paper, we provide a Self-Evaluation High-throughput Computing framework (SEHC). The framework introduces an automatic self-evaluation filtering mechanism, which is based on machine learning, for high-throughput computing architectures to stop unexpected materials calculation tasks in advance during high-throughput calculation. The time-consuming high-throughput computing process is disassembled into several finer-grained high-throughput Stages. Multiple high-throughput Stages with the same standard design specifications can be assembled into a Pipeline model. Combined with the public service like data storage and system monitoring, the SEHC with a “Stage-Pipeline-Framework” three-tier structure is formed. To search for diamond-like structures with higher group velocity in a space of 254 compounds, a SEHC-based prototype was implemented. The experiment result shows that this prototype achieved a significant improvement in efficiency by reducing the amount of invalid computation remarkably.
ISSN:0921-5107
1873-4944
DOI:10.1016/j.mseb.2019.114474