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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
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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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Research Article
Thermomechanical Characterization of Massakory Clay Reinforced by Seyal Gum Arabic for Sustainable Building Applications
Issue:
Volume 14, Issue 3, June 2025
Pages:
72-78
Received:
13 May 2025
Accepted:
28 May 2025
Published:
23 June 2025
DOI:
10.11648/j.ijmsa.20251403.13
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Abstract: This study focuses on determining the thermomechanical properties of clay extracted from the Kangartoulo brickyard site in Massakory, reinforced with seyal gum arabic for sustainable development construction applications. Raw earth has been used and is used in the construction field for its abundance and low environmental impact. The experimental results of thermal tests show that a decrease in thermal conductivity and thermal effusivity is observed for a gum arabic content below 4%, followed by a slight increase beyond this threshold and also by varying the compaction pressure from 3 to 6 MPa. Regarding mechanical properties, the simple compressive strength almost tripled with the addition of gum arabic from 0 to 8%, it increased from 3.2 to 9.4 MPa for compaction pressures ranging from 3 to 6 MPa. The three-point bending strength has also been improved, increasing from 0.78 to 2.10 MPa for the same binder contents. The numerical simulation carried out with the RETScreen software made it possible to estimate an energy gain of 29% for the optimal formulation retained compared to cementitious materials.
Abstract: This study focuses on determining the thermomechanical properties of clay extracted from the Kangartoulo brickyard site in Massakory, reinforced with seyal gum arabic for sustainable development construction applications. Raw earth has been used and is used in the construction field for its abundance and low environmental impact. The experimental r...
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Research Article
Influence of Window Layers on the Spectral Evolution of the Total Current Flowing Through a Solar Cell Based on Lead-Free Perovskite Materials
Saliou Seck*,
Alioune Sow,
Mamadou Salif Mane,
Modou Faye,
El Hadji Mamadou Keita,
Amadou Ndiaye,
Bachirou Ndiaye,
Babacar Mbow,
Cheikh Sene
Issue:
Volume 14, Issue 3, June 2025
Pages:
79-88
Received:
14 May 2025
Accepted:
28 May 2025
Published:
23 June 2025
DOI:
10.11648/j.ijmsa.20251403.14
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Abstract: In this work a modeling study of photovoltaic devices based on lead-free CH3 NH3 Sn(1-y)Gey I3 perovskite materials, is carried out with different window layers. The transport window layers materials used are ZnO, TiO2 and SnO2. They enable minority charge carriers to be transported to the active perovskite layer and collected there. The study also focuses on the influence of geometric parameters such as the diffusion length of minority charge carriers, the surface recombination velocity and the thickness of the window layers on the performance of the devices. The photovoltaic devices have first been modeled using the ZnO window layer in the multijunction ZnO(n+)/Cu2O(n)/CH3NH3Sn(1-y)GeyI3(p) structure. The ZnO window layer was then successively substituted by the TiO2 and SnO2 layers, leading thus to TiO2 (n+)/Cu2 O(n)/CH3NH3Sn(1-y)GeyI3(p) and SnO2 (n+)/Cu2 O(n)/ CH3 NH3 Sn(1-y)Gey I3 (p) photovoltaic structures, respectively. The CH3 NH3 Sn(1-y)Gey I3 perovskite absorber layers considered in these structures contain a germanium content varying from 0 to 1. Our study showed that the best performances are obtained for a germanium content of around 0.25, corresponding to 65.8%, 49.7% and 64.5%, for ZnO, SnO2 and TiO2 window layers, respectively.
Abstract: In this work a modeling study of photovoltaic devices based on lead-free CH3 NH3 Sn(1-y)Gey I3 perovskite materials, is carried out with different window layers. The transport window layers materials used are ZnO, TiO2 and SnO2. They enable minority charge carriers to be transported to the active perovskite layer and collected there. The study also...
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Research Article
Design Optimization of the Heat-exchange Pressure Vessel by Numerical Simulation Analysis
Toai Dinh Vu,
Xuan Thi Tran*
Issue:
Volume 14, Issue 3, June 2025
Pages:
89-105
Received:
16 May 2025
Accepted:
3 June 2025
Published:
23 June 2025
DOI:
10.11648/j.ijmsa.20251403.15
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Abstract: Currently, the thickness of the elements of pressure vessels is often determined by calculation according to the formulas in ASME Code Section VIII or numerical simulation using mechanical calculation software. Calculations according to the ASME Code often give results with much excess durability, while conventional numerical simulation calculations will take a lot of time due to manual detection and depend heavily on the experience of the designer. Both of the above methods are very difficult to determine the optimal value of design variables (thickness of the structure) to ensure the most workability and material savings. This paper presents an automatic calculation method to determine the optimal thickness of the elements of a pressure vessel through a new self-developed simulation algorithm, in which all the stages of a conventional numerical simulation problem have been wrapped into an automatic loop “– modeling – solve – evaluate results – remodeling –”. A computer program written in the APDL language of ANSYS software, based on this new algorithm, has automatically determined the optimal value of the thicknesses of the pressure vessel regardless of its initially selected preliminary values. That is, starting from any preliminary value of the design variables, the computer program will automatically calculate to determine the optimal value of the design variables that ensure the structure satisfies the working conditions and has the smallest mass or volume. Applying this method, the volume of the considered heat-exchange pressure vessel was reduced from 0.50658 m3 to 0.41970 m3, which means that 0.08688 m3 or 677.664 kg of steel was saved.
Abstract: Currently, the thickness of the elements of pressure vessels is often determined by calculation according to the formulas in ASME Code Section VIII or numerical simulation using mechanical calculation software. Calculations according to the ASME Code often give results with much excess durability, while conventional numerical simulation calculation...
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