Compression-property-software

Deep learning-based predictor of compressive mechanical properties for high-entropy alloys software V1.0

Introduction

"Deep learning-based predictor of compressive mechanical properties for high-entropy alloys software V1.0" provides an intuitive and easy-to-use user interface. It predicts and analyzes the mechanical properties of high-entropy alloys under compressive stress based on the input alloy composition.

Main Objectives

• Performance Prediction: The software can predict key mechanical indicators such as yield strength and elongation of the alloy under specific compressive conditions.
• User-Friendly Interaction: The software offers an intuitive graphical user interface that allows easy data input, execution of predictions, and interpretation of results.

Features

1. Descriptor Calculation

Overview: This feature allows users to input specific alloy compositions and automatically calculates a series of important material descriptors based on these compositions.

Details: Descriptors include, but are not limited to, atomic size difference, electronegativity, melting point, and other key parameters that influence material properties.

2. Mechanical Properties Prediction

Overview: This feature uses deep learning models to predict the compressive mechanical properties of a given alloy composition, such as yield strength and elongation.

Details: After users input the specific composition of the alloy, the software employs trained neural network models to estimate the material's response under compressive stress.

3. Elemental Variation Analysis

Overview: Users can analyze how the variation of single or multiple elements affects the overall performance of the alloy.

Details: This feature provides options to adjust the content of single or multiple elements and shows how these adjustments impact the mechanical properties of the alloy.