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A machine learning accelerated distributed task management system(Malac-Distmas)and its application in high-throughput CALPHAD computation aiming at efficient alloy design
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作者 Jianbao Gao Jing Zhong Guangchen liu +3 位作者 Shenglan Yang Bo Song Lijun Zhang zuming liu 《Advanced Powder Materials》 2022年第1期76-87,共12页
High-throughput calculations/simulations are the prerequisite for the efficient design of high-performance materials.In this paper,a machine learning accelerated distributed task management system(Malac-Distmas)was de... High-throughput calculations/simulations are the prerequisite for the efficient design of high-performance materials.In this paper,a machine learning accelerated distributed task management system(Malac-Distmas)was developed to realize the high-throughput calculations(HTCs)and storage of various data.The machine learning was embedded in Malac-Distmas to densify the output data,reduce the amount of calculation and achieve the acceleration of high-throughput calculations.Based on the Malac-Distmas coupling with CALPHAD software,HTCs of thermodynamics,kinetics,and thermophysical properties,including Gibbs free energy,phase diagram,Scheil-Gulliver solidification simulation,thermodynamic properties,thermophysical properties,diffusion simulation,and precipitation simulation,have been performed for demonstration.Furthermore,it is highly anticipated that the Malac-Distmas can also be coupled with any calculation/simulation software/code,which provides a console model to achieve different types of HTCs for efficient alloy design. 展开更多
关键词 High throughput CALPHAD Machine learning Thermodynamics Kinetics Alloy design
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