Research Article
Development of a Machine Learning Model for Predicting the Structural and Optical Properties of Nanomaterials Based on Quantum-Mechanical Simulations
Issue:
Volume 14, Issue 3, June 2025
Pages:
60-66
Received:
7 May 2025
Accepted:
21 May 2025
Published:
3 June 2025
DOI:
10.11648/j.ijmsa.20251403.11
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Abstract: The rapid advancement of nanotechnology has enabled the development of materials with unique properties that differ significantly from their bulk counterparts. Understanding and predicting the properties of nanomaterials, such as their electronic, optical, and mechanical characteristics, is crucial for their application in fields like electronics, energy storage, and catalysis. However, the computational methods used to predict these properties, particularly through quantum mechanical simulations such as Density Functional Theory (DFT), are computationally expensive and time-consuming, especially when applied to large datasets of nanomaterials. This paper proposes a novel approach that integrates machine learning (ML) techniques with DFT simulations to predict the structural and optical properties of nanomaterials. By utilizing a dataset derived from DFT calculations, we train and evaluate multiple machine learning models, including Random Forest, Support Vector Machine (SVM), and Deep Neural Networks (DNN), to predict key properties such as band gap, conductivity, and optical absorption. The goal is to develop a model that reduces the computational burden of traditional simulation methods while maintaining high accuracy and generalizability. The models were trained on a synthetic dataset that simulates the composition, size, and crystal structure of nanomaterials, with target properties generated based on these features. We evaluated the performance of the models using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2. Results show that the DNN model provides the best predictive accuracy, closely followed by the Random Forest model, while the SVM model demonstrated lower performance in this context. Additionally, feature importance analysis revealed that material composition, particle size, and crystal structure were the most influential factors in determining the predicted properties of the nanomaterials. This research demonstrates the potential of machine learning to accelerate the discovery of new nanomaterials by providing a fast and scalable way to predict their properties. By combining the predictive power of ML with quantum mechanical simulations, this study offers an efficient framework for material discovery that can be applied to a wide range of nanomaterial systems.
Abstract: The rapid advancement of nanotechnology has enabled the development of materials with unique properties that differ significantly from their bulk counterparts. Understanding and predicting the properties of nanomaterials, such as their electronic, optical, and mechanical characteristics, is crucial for their application in fields like electronics, ...
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Research Article
Energy-efficient Thermal Insulation Material for District Heating Pipelines
Chantsaldulam Erdenechuluun
,
Tserendolgor Dugargaramjav*
Issue:
Volume 14, Issue 3, June 2025
Pages:
67-71
Received:
21 April 2025
Accepted:
3 May 2025
Published:
12 June 2025
DOI:
10.11648/j.ijmsa.20251403.12
Downloads:
Views:
Abstract: In recent years, Mongolia has experienced a shortage of district heating sources and networks, primarily due to intensive construction, including apartment buildings. With urbanization and economic growth, new buildings are being built at a rapid pace, requiring connections to the district heating (DH) system. Recent data shows that the annual growth rate of heat consumption has increased by approximately 3 to 5 percent compared to previous periods. As a result, one of the key tasks for our energy sector is to implement a cost-saving policy to reduce heat losses in the distribution network. Additionally, around 30 percent of Ulaanbaatar's heating networks are outdated and cannot be swiftly replaced due to economic and time constraints. This paper focuses on experimental studies of heat losses within district heating (DH) systems' pipe networks. In these heat networks, various thermal insulating materials are used. Over time, the insulation around the pipelines deteriorates, and due to wear and environmental factors, it fails to meet technical requirements, leading to a significant increase in heat loss beyond calculated values. Effectively implementing energy efficiency in a district heating system requires a comprehensive understanding of the energy performance of the pipe networks, which can be achieved through energy audit techniques. Using a drone equipped with a thermal camera, we assessed pipeline heat loss and damage in real-time and dynamic conditions. Additionally, we compared different pipeline insulation materials and conducted feasibility studies on utilizing high-density pre-insulated polyurethane foam insulation boards. Our proposal indicates that the heat loss from the insulation panels will be 1.7 times lower than the reference value, resulting in a 30% energy saving, as confirmed by both technical and economic calculations.
Abstract: In recent years, Mongolia has experienced a shortage of district heating sources and networks, primarily due to intensive construction, including apartment buildings. With urbanization and economic growth, new buildings are being built at a rapid pace, requiring connections to the district heating (DH) system. Recent data shows that the annual grow...
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