Changxin Wang

TL;DR: In this article, a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system was proposed.

TL;DR: In this paper, a genetic algorithm was used to 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).

TL;DR: In this article, the authors demonstrate a relationship derived in terms of the electronegative difference of elements to characterize solid solution strengthening (SSS) for single-phase high entropy alloys.

TL;DR: In this paper, the authors adopt machine learning methods to discover the most important materials descriptors for properties of ferroelectric materials and propose a machine learning strategy based on their descriptors to predict the phase coexistence.

TL;DR: In this article , Zhao et al. used active learning to discover novel cyan-green garnet phosphors, wavelength and thermal stability machine learning models were built by constructing reasonable features and 25 samples were selected for preparation and characterization by multiobjective optimization based on active learning.