Machine Learning Accelerated, High Throughput, Multi‐Objective Optimization of Multiprincipal Element Alloys (Small 42/2021). Issue 42 (21st October 2021)

• Record Type: Journal Article
• Authors: Guo, Tian; Wu, Lianping; Li, Teng
• Is Part Of: Small. Volume 17:Issue 42(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-21
• Subjects: machine learning -- molecular dynamic simulations -- multi‐objective optimization -- multiprincipal element alloys; Nanotechnology -- Periodicals; Nanoparticles -- Periodicals; Microtechnology -- Periodicals; 620.5
• DOI: 10.1002/smll.202170222
• Languages: English
• ISSNs: 1613-6810

Abstract

Abstract : Multiprincipal Element Alloys In article number 2102972, Teng Li and co‐workers present a highly efficient design strategy of multiprincipal element alloys (MPEAs) through a coherent integration of molecular dynamic simulation, machine learning algorithms and genetic algorithm. Such a design strategy not only yields remarkable precision of prediction of the critical resolved shear stress and Young's modulus of CoNiCrFeMn MPEAs with an impressively low error, but also enables a drastic 12 600‐fold reduction of prediction time in comparison with pure atomic simulations. The multi‐objective genetic algorithm further helps identify 100 optimal MPEA compositions with both high critical resolved shear stress and high stiffness.