Quantum Mechanics-Guided Machine Learning Framework for Cost-Efficient Prediction and Economic Optimization of the Mechanical, Thermal, and Structural Properties of Advanced Nanomaterials

Abstract

The accelerating development of advanced nanomaterials offers major opportunities for aerospace structures, nanoelectronics, thermal management, energy systems, and next-generation mechanical devices. However, their commercialization is limited by the high cost, long duration, and resource intensity of experimental characterization and exhaustive quantum-mechanical simulations. This study develops a Quantum Mechanics-Guided Machine Learning (QM-ML) framework for cost-efficient prediction and economic optimization of mechanical, thermal, and structural properties of advanced nanomaterials. Density functional theory calculations were combined with material composition, atomic-scale structure, bonding characteristics, formation energy, cohesive energy, and electronic descriptors to create a physics-informed feature space. After normalization, correlation analysis, and feature selection, support vector regression, random forest, gradient boosting, XGBoost, and artificial neural network models were trained and evaluated using R², root mean square error, mean absolute error, and cross-validation, with computational demand and screening efficiency included as economic criteria. The framework achieved R² values of 0.967 for Young’s modulus, 0.954 for thermal conductivity, and 0.978 for structural stability, outperforming models without quantum-mechanical descriptors. It reduced average prediction error by approximately 31.6% and decreased computational evaluation requirements by 68.4% compared with exhaustive quantum-mechanical screening. Feature-importance analysis identified cohesive energy, atomic coordination, bond characteristics, formation energy, and electronic-structure descriptors as key determinants of performance. Cost-aware multi-objective optimization further identified candidates with 18.7% improved mechanical performance and 15.3% enhanced thermal performance while maintaining structural stability. Overall, the QM-ML framework provides an accurate, interpretable, and economically efficient approach for accelerating nanomaterial discovery and scalable materials development.

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