Thermodynamics, a cornerstone of physics, focuses on the interplay between heat, work, temperature, and the statistical behavior of system s. In recent decades, the field has witnessed significant advancements in modeling techniques, integrating classical theories with modern computational methods. This paper reviews the evolution of thermodynamic modeling, highlighting both the limitations of traditional approaches and the emergence of innovative computational strategies such as molecular dynamics, Monte Carlo simulations, and machine learning. Classical thermodynamics, grounded in macroscopic observations, has established fundamental principles that govern energy and matter. However, traditional models often fall short in accurately predicting the behavior of complex systems, especially at the molecular or atomic level. Computational techniques have surfaced as powerful tools, enabling researchers to simulate intricate systems that were previously intractable, thereby enhancing our understanding of thermodynamic phenomena. The integration of classical and computational approaches has led to the development of hybrid models that leverage the strengths of both domains. These hybrid frameworks facilitate the exploration of complex phenomena, allowing for a more comprehensive understanding of thermodynamic systems and their applications in materials science, energy systems, and biological processes. Furthermore, the advent of machine learning technologies has provided new avenues for optimization and predictive modeling, significantly improving the performance of energy conversion systems. Despite these advancements, challenges remain. Issues such as data quality, system complexity, and interpret ability of machine learning models necessitate ongoing research. This paper employs a comprehensive literature review methodology to synthesize findings from various sources, identifying key themes and trends in thermodynamic modeling. It emphasizes the importance of interdisciplinary approaches that combine thermodynamics with fields like materials science and engineering. Ultimately, this study underscores the significance of bridging classical thermodynamic principles with computational techniques. It posits that continued research in this area will not only deepen our understanding of thermodynamic systems but also pave the way for innovations that address pressing global challenges, including energy efficiency and sustainability. Through this integration, the potential for breakthroughs in understanding the fundamental principles governing energy and matter is immense, setting the stage for future advancements in the field.
| Published in | European Journal of Biophysics (Volume 13, Issue 1) |
| DOI | 10.11648/j.ejb.20251301.11 |
| Page(s) | 1-9 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Thermodynamics, Molecular Dynamics, Monte Carlo Simulations, Machine Learning, Modeling, Energy Systems, Materials Science
| [1] | Allen, M. P., & Tildesley, D. J. (1987). Computer Simulation of Liquids. Oxford University Press. |
| [2] | Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. |
| [3] | Binder, K., & Heermann, D. W. (2010). Monte Carlo Simulation in Statistical Physics: An Introduction. Springer. |
| [4] | Callen, H. B. (1985). Thermodynamics, and an Introduction to Thermostatistics. Wiley. |
| [5] | Ceriotti, M., & Parrinello, M. (2018). "Machine Learning in Quantum Chemistry." Nature Reviews Chemistry, 2(4), 1-15. |
| [6] | Frenkel, D., & Smit, B. (2002). Understanding Molecular Simulation: From Algorithms to Applications. Academic Press. |
| [7] | Schütt, K. T., et al. (2017). "Quantum-chemical insights from deep tensor neural networks." *Nature Communications*, 8(1), 13890. |
| [8] | Wang, H., et al. (2020). "Molecular dynamics simulations of phase transitions in materials." Journal of Materials Science, 55(1), 1-15. |
| [9] | Zhang, Y., et al. (2019). "Molecular dynamics simulations of nanomaterials: A review." Materials Today, 22, 1-12. |
| [10] | Burke, K., & Pople, J. A. (2007). "Perspective on Density Functional Theory." The Journal of Chemical Physics, 126(15), 150901. |
| [11] | Cai, J., et al. (2020). "Machine Learning for Molecular Dynamics: A Review." *Chemical Reviews*, 120(15), 7306-7330. |
| [12] | Chen, H., et al. (2018). "A Review of Machine Learning in Energy Systems: A Focus on the Energy Efficiency and Optimization." Renewable and Sustainable Energy Reviews, 82, 1621-1634. |
| [13] | Cleveland, W. S., & Loader, C. (1996). "Smoothing by Local Regression: Principles and Methods." Statistical Science, 11(4), 399-414. |
| [14] | Deng, H., et al. (2021). "A Review of Machine Learning in Energy Storage Systems." Renewable and Sustainable Energy Reviews*, 135, 110-120. |
| [15] | Friedman, J., Hastie, T., & Tibshirani, R. (2001). The Elements of Statistical Learning. Springer Series in Statistics. |
| [16] | Gao, Y., et al. (2020). "Data-driven Approaches for Energy Conversion Systems: A Review." Energy Reports, 6, 1433-1446. |
| [17] | Ghasemi, S., et al. (2021). "Machine Learning for Energy Management in Smart Grids: A Review." *IEEE Transactions on Smart Grid*, 12(2), 1568-1580. |
| [18] | Hansen, J. P., & McDonald, I. R. (2006). Theory of Simple Liquids. Academic Press. |
| [19] | Karniadakis, G. E., et al. (2021). "Machine Learning and Data-driven Methods for Fluid Mechanics." *Applied Mechanics Reviews*, 73(3), 1-28. |
| [20] | LeCun, Y., Bengio, Y., & Haffner, P. (1998). "Gradient-based Learning Applied to Document Recognition." Proceedings of the IEEE, 86(11), 2278-2324. |
| [21] | Miller, T. F., et al. (2020). "Machine Learning for Molecular Simulations." Nature Reviews Chemistry, 4(4), 1-15. |
| [22] | Neal, R. M. (1996). Bayesian Learning for Neural Networks. Springer Series in Statistics. |
| [23] | Rupp, M., et al. (2012). "Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning." Physical Review Letters, 108(5), 058301. |
APA Style
Tolasa, D. G. (2025). Advancements in Thermodynamic Modeling: Bridging Classical Theory and Computational Techniques. European Journal of Biophysics, 13(1), 1-9. https://doi.org/10.11648/j.ejb.20251301.11
ACS Style
Tolasa, D. G. Advancements in Thermodynamic Modeling: Bridging Classical Theory and Computational Techniques. Eur. J. Biophys. 2025, 13(1), 1-9. doi: 10.11648/j.ejb.20251301.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ejb.20251301.11,
author = {Diriba Gonfa Tolasa},
title = {Advancements in Thermodynamic Modeling: Bridging Classical Theory and Computational Techniques
},
journal = {European Journal of Biophysics},
volume = {13},
number = {1},
pages = {1-9},
doi = {10.11648/j.ejb.20251301.11},
url = {https://doi.org/10.11648/j.ejb.20251301.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ejb.20251301.11},
abstract = {Thermodynamics, a cornerstone of physics, focuses on the interplay between heat, work, temperature, and the statistical behavior of system s. In recent decades, the field has witnessed significant advancements in modeling techniques, integrating classical theories with modern computational methods. This paper reviews the evolution of thermodynamic modeling, highlighting both the limitations of traditional approaches and the emergence of innovative computational strategies such as molecular dynamics, Monte Carlo simulations, and machine learning. Classical thermodynamics, grounded in macroscopic observations, has established fundamental principles that govern energy and matter. However, traditional models often fall short in accurately predicting the behavior of complex systems, especially at the molecular or atomic level. Computational techniques have surfaced as powerful tools, enabling researchers to simulate intricate systems that were previously intractable, thereby enhancing our understanding of thermodynamic phenomena. The integration of classical and computational approaches has led to the development of hybrid models that leverage the strengths of both domains. These hybrid frameworks facilitate the exploration of complex phenomena, allowing for a more comprehensive understanding of thermodynamic systems and their applications in materials science, energy systems, and biological processes. Furthermore, the advent of machine learning technologies has provided new avenues for optimization and predictive modeling, significantly improving the performance of energy conversion systems. Despite these advancements, challenges remain. Issues such as data quality, system complexity, and interpret ability of machine learning models necessitate ongoing research. This paper employs a comprehensive literature review methodology to synthesize findings from various sources, identifying key themes and trends in thermodynamic modeling. It emphasizes the importance of interdisciplinary approaches that combine thermodynamics with fields like materials science and engineering. Ultimately, this study underscores the significance of bridging classical thermodynamic principles with computational techniques. It posits that continued research in this area will not only deepen our understanding of thermodynamic systems but also pave the way for innovations that address pressing global challenges, including energy efficiency and sustainability. Through this integration, the potential for breakthroughs in understanding the fundamental principles governing energy and matter is immense, setting the stage for future advancements in the field.
},
year = {2025}
}
TY - JOUR T1 - Advancements in Thermodynamic Modeling: Bridging Classical Theory and Computational Techniques AU - Diriba Gonfa Tolasa Y1 - 2025/03/21 PY - 2025 N1 - https://doi.org/10.11648/j.ejb.20251301.11 DO - 10.11648/j.ejb.20251301.11 T2 - European Journal of Biophysics JF - European Journal of Biophysics JO - European Journal of Biophysics SP - 1 EP - 9 PB - Science Publishing Group SN - 2329-1737 UR - https://doi.org/10.11648/j.ejb.20251301.11 AB - Thermodynamics, a cornerstone of physics, focuses on the interplay between heat, work, temperature, and the statistical behavior of system s. In recent decades, the field has witnessed significant advancements in modeling techniques, integrating classical theories with modern computational methods. This paper reviews the evolution of thermodynamic modeling, highlighting both the limitations of traditional approaches and the emergence of innovative computational strategies such as molecular dynamics, Monte Carlo simulations, and machine learning. Classical thermodynamics, grounded in macroscopic observations, has established fundamental principles that govern energy and matter. However, traditional models often fall short in accurately predicting the behavior of complex systems, especially at the molecular or atomic level. Computational techniques have surfaced as powerful tools, enabling researchers to simulate intricate systems that were previously intractable, thereby enhancing our understanding of thermodynamic phenomena. The integration of classical and computational approaches has led to the development of hybrid models that leverage the strengths of both domains. These hybrid frameworks facilitate the exploration of complex phenomena, allowing for a more comprehensive understanding of thermodynamic systems and their applications in materials science, energy systems, and biological processes. Furthermore, the advent of machine learning technologies has provided new avenues for optimization and predictive modeling, significantly improving the performance of energy conversion systems. Despite these advancements, challenges remain. Issues such as data quality, system complexity, and interpret ability of machine learning models necessitate ongoing research. This paper employs a comprehensive literature review methodology to synthesize findings from various sources, identifying key themes and trends in thermodynamic modeling. It emphasizes the importance of interdisciplinary approaches that combine thermodynamics with fields like materials science and engineering. Ultimately, this study underscores the significance of bridging classical thermodynamic principles with computational techniques. It posits that continued research in this area will not only deepen our understanding of thermodynamic systems but also pave the way for innovations that address pressing global challenges, including energy efficiency and sustainability. Through this integration, the potential for breakthroughs in understanding the fundamental principles governing energy and matter is immense, setting the stage for future advancements in the field. VL - 13 IS - 1 ER -
Department of Physics, Assosa University, Assosa, Ethiopia
Information