Two-dimensional Piezomagnetic materials have special properties when placed into certain geometries, and the goal is to find a new one that may define a unique material property. Currently, there are 15 types of stable pentagonal geometries found, the last being by the team Dr. Houlong Zhuang leads. Finding other possibilities of these geometries may lead
Researchers apply Machine learning (ML) algorithms to efficiently explore phase selection rules using a comprehensive experimental dataset consisting of 401 different HEAs including 174 SS, 54 IM, and 173 SS+IM phases. We adopt three different ML algorithms: K-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). The purpose of the work is
Effective utilization of machine learning could accelerate the discovery of new energy-related materials.
Exploring the properties of 2D nanomaterials could lead to new methods of energy harvesting and storage.