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

Abstract

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.