# Artificial Neural Network Algorithm based Skyrmion Material Design of Chiral Crystals

## TL;DR

This research employs deep learning, specifically an artificial neural network, to predict and design chiral crystal materials with skyrmions, advancing the computational tools for material discovery.

## Contribution

It introduces a novel ANN-based approach for chiral crystal design, integrating probabilistic classification with deep learning for accurate prediction.

## Key findings

- ANN outperforms probabilistic classifier in accuracy
- Deep learning model effectively predicts chiral crystal formation
- Proposes a new software tool for crystal design

## Abstract

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.