Machine Learning Accelerated, High Throughput, Multi‐Objective Optimization of Multiprincipal Element Alloys. Issue 42 (15th September 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: 2021-09-15
• Subjects: machine learning -- molecular dynamic simulations -- multi‐objective optimization -- multiprincipal element alloys; Nanotechnology -- Periodicals; Nanoparticles -- Periodicals; Microtechnology -- Periodicals; 620.5
• DOI: 10.1002/smll.202102972
• Languages: English
• ISSNs: 1613-6810

Abstract

Abstract: Multiprincipal element alloys (MPEAs) have gained surging interest due to their exceptional properties unprecedented in traditional alloys. However, identifying an MPEA with desired properties from a huge compositional space via a cost‐effective design remains a grand challenge. To address this challenge, the authors present a highly efficient design strategy of MPEAs through a coherent integration of molecular dynamics (MD) simulation, machine learning (ML) algorithms, and genetic algorithm (GA). The ML model can be effectively trained from 54 MD simulations to predict the stiffness and critical resolved shear stress (CRSS) of CoNiCrFeMn alloys with a relative error of 2.77% and 2.17%, respectively, with a 12 600‐fold reduction of computation time. Furthermore, by combining the highly efficient ML model and a multi‐objective GA, one can predict 100 optimal compositions of CoNiCrFeMn alloys with simultaneous high stiffness and CRSS, as verified by 100 000 ML‐accelerated predictions. The highly efficient and precise design strategy can be readily adapted to identify MPEAs of other principal elements and thus substantially accelerate the discovery of other high‐performance MPEA materials. Abstract : Identifying a multiprincipal element alloy (MPEA) with desired properties from a huge compositional space remains a grand challenge. This paper presents a highly efficient design strategy of MPEAs through a coherent integration of molecular dynamics simulation, machine learning algorithms, and genetic algorithm. The design strategy can accelerate the discovery of high‐performance MPEA materials.