Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

By Y. Zhang, Cheng Wen, Changxin Wang, S. Antonov, D. Xue, Yang Bai, Yanjing Su. 2020

394 citations recorded by the source corpus.

Abstract Materials informatics employs machine learning (ML) models to map the relationship between a targeted property and various materials descriptors, providing new avenues to accelerate the discovery of new materials. However, the possible ML models and materials descriptors are numerous, and a rational recipe to rapidly choose the best combination of the two is needed. In the present study, we propose a systematic framework that utilizes a genetic algorithm (GA) to efficiently select the ML model and materials descriptors from a huge number of alternatives and demonstrated its efficiency on two phase formation problems in high entropy alloys (HEAs). The optimized classification model allows an accuracy for identifying solid-solution and non-solid-solution HEAs to be up to 88.7% and further for recognizing body-centered-cubic (BCC), face-centered-cubic (FCC), and dual-phase HEAs to reach 91.3%. Furthermore, by employing an active learning approach, several HEAs with largest classification uncertainties were selected, experimentally synthesized and phase-identified, and augmented to the initial dataset to iteratively improve the ML model. The method serves as a general algorithm to select materials descriptors and ML models for various material problems including classification and optimization of targeted properties.

Subfields: Aerospace Engineering, Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/dbd6fa1cea36eee989352ef3197787a23ce04017

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In Aerospace Engineering

  1. Xianglin Liu
  2. Zongrui Pei
  3. Jiaxin Zhang

In Materials Chemistry

  1. Xianglin Liu
  2. Zongrui Pei
  3. Jiaxin Zhang

In Mechanical Engineering

  1. Xianglin Liu
  2. Zongrui Pei
  3. Jiaxin Zhang

Citations in the corpus