Algorithmic Modeling and Machine Learning Analysis of Atomic Structure Properties Using High-Dimensional Feature Representations A Machine Learning Approach to Multimodal analysis

Accurate modeling of atomic structure properties is a fundamental challenge in computational physics, materials science, and data-driven engineering applications. Traditional analytical and simulation-based approaches often struggle to scale efficiently when faced with high-dimensional atomic descriptors and nonlinear inter-atomic relationships. This study presents a robust algorithmic and machine learning framework for predicting atomic structure properties using structured, high-dimensional feature representations. Multiple supervised learning models are evaluated under a unified experimental framework to assess their ability to capture complex atomic behavior. The study emphasizes methodological rigor, statistical validation, and comparative performance analysis rather than isolated model performance. Results demonstrate that ensemble-based and nonlinear learning approaches significantly outperform linear baselines, highlighting their suitability for atom-level predictive modeling.