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

Description

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.

Steps to reproduce

Modeling atomic structure properties with high accuracy is critical for advancements in materials science, condensed matter physics, and nano-engineering. Conventional physics-based methods, while theoretically grounded, often face scalability and computational constraints when applied to complex atomic systems. This paper proposes a data-driven algorithmic framework for atomic structure analysis using high-dimensional feature representations and supervised machine learning models. A diverse set of learning algorithms is systematically evaluated to capture nonlinear atomic interactions and property dependencies. Model performance is assessed using regression- and classification-oriented metrics under a consistent validation strategy. Experimental results reveal that ensemble and nonlinear models achieve superior predictive stability and accuracy compared to linear approaches. The findings demonstrate the potential of machine learning-driven atomic modeling as a complementary tool to traditional computational methods, enabling scalable and efficient atomic-level predictions.

Institutions

• North South University

  Dhaka Division, Dhaka
• Independent University

  Dhaka Division, Dhaka

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