Prediction of Electronic and Structural Properties in Two-Dimensional Materials employing Spiking Deep Residual Network

Two-dimensional (2D) materials exhibit unique electronic and structural properties, but accurately predicting these characteristics remains challenging due to complex feature interactions and computational constraints. This manuscript proposes a Prediction of Electronic and Structural Properties in Two-Dimensional Materials employing Spiking Deep Residual Network (PESP-2D-SDRN). Initially, Input data is sourced from the 2DMatPedia dataset and preprocessed using the Multivariate Fast Iterative Filter (M-FIF), which effectively removes noise. Relevant features from preprocessed data are then selected using the Oscillating Spider Monkey Optimization Algorithm (OSMO), enhancing model focus on informative inputs. The selected features are given into the Spiking Deep Residual Network (SDRN) for precise property prediction across diverse material classes, including semiconducting, metallic, magnetic, insulating, and topological systems. Implemented in MATLAB, the proposed method demonstrates superior performance, achieving 98.5% precision and 99.4% accuracy. These results confirm that PESP-2D-SDRN offers a robust and computationally efficient solution for high-dimensional material property prediction.