Artificial Neural Network Algorithm based Skyrmion Material Design of Chiral Crystals
This work addresses the challenge of material design for skyrmions, which are important in spintronics, but it appears incremental as it compares ANN to an existing probabilistic method without reporting specific performance numbers.
The research tackled the problem of designing chiral crystals for skyrmion materials by developing a deep learning method, specifically an Artificial Neural Network (ANN), which was tested against a probabilistic classifier to predict chirality accuracy.
The model presented in this research predicts ideal chiral crystal and propose a new direction of designing chiral crystals. Skyrmions are topologically protected and structurally assymetric materials with an exotic spin composition. This work presents deep learning method for skyrmion material design of chiral crystals. This paper presents an approach to construct a probabilistic classifier and an Artificial Neural Network(ANN) from a true or false chirality dataset consisting of chiral and achiral compounds with 'A' and 'B' type elements. A quantitative predictor for accuracy of forming the chiral crystals is illustrated. The feasibility of ANN method is tested in a comprehensive manner by comparing with probalistic classifier method. Throughout this manuscript we present deep learnig algorithm design with modelling and simulations of materials. This research work elucidated paves a way to develop sophisticated software tool to make an indicator of crystal design.