Optimizing Neural Networks for Enhanced Material Property Predictions: Insights from Bulk Modulus Analysis

Abstract

This research presents a deep learning model designed to accurately compute material properties, with a specific focus on the bulk modulus. This study places significant emphasis on hyperparameter optimization, involving adjustments to batch size, learning rate, hidden layer, and neuron count. The dataset, comprising 7107 diverse materials, undergoes thorough preprocessing, which includes outlier removal and the extraction of elemental property descriptors using the matminer library and the Magpie dataset. The core model utilized in this research is an Artificial Neural Network (ANN), with the descriptors serving as crucial input features. Model performance assessment is conducted by using the Mean Absolute Error (MAE) as a quantitative metric, providing insights into predictive accuracy. This research also employs sensitivity analysis to scrutinize the significance of 132 features in predicting the bulk modulus property, contributing to an understanding of material behavior dynamics and facilitating model optimization. The results highlight the impact of neuron count, layer depth, learning rate, and batch size on prediction accuracy. Furthermore, feature importance analysis underscores the critical role of specific material properties, with mean covalent radius emerging as the most influential factor in predicting the bulk modulus. These discoveries provide guidelines for optimizing neural network configurations and material property descriptors for predicting material elasticity.