Kharazmi UniversityJournal Of Modern Research Physics2588-492111120260823An Interpretable Machine Learning Framework for Predicting the Electrochemical Properties of Carbon-Based Electrode Materials in Lithium-Ion BatteriesAn Interpretable Machine Learning Framework for Predicting the Electrochemical Properties of Carbon-Based Electrode Materials in Lithium-Ion Batteries1121177410.22034/jmrph.2026.137621.1000FAFardinTaghizadehDepartment of Physics, Faculty of Basic Sciences, Yasuj University, Yasuj, Iran0000-0002-2506-1921ElhamZareDepartment of Physics, Faculty of Basic Sciences, Yasuj University, Yasuj, Iran0009-0004-5685-8783Journal Article20260729In this study, machine learning methods based on quantum chemical descriptors derived from Density Functional Theory (DFT) calculations were employed to predict the redox potentials of redox-active organic molecules. Electronic descriptors extracted from DFT calculations served as inputs for various machine learning models, whose performances were evaluated using R2, MAE, and RMSE metrics. The results demonstrated that the SVR model achieved the highest predictive accuracy, with a coefficient of determination (R2) of 0.974 and the lowest error rate, while the Stacking model exhibited closely comparable and stable performance. Correlation analysis, feature importance, and SHAP values revealed that electron transfer-related descriptors, particularly electron affinity, play the most significant role in predicting redox potentials. The consistency between ML results and quantum chemical concepts indicates that the proposed framework achieves high predictive accuracy alongside robust interpretability. This study highlights that the integration of DFT calculations and machine learning can provide an efficient approach for the rapid screening and targeted design of redox-active organic molecules with optimized electrochemical properties.In this study, machine learning methods based on quantum chemical descriptors derived from Density Functional Theory (DFT) calculations were employed to predict the redox potentials of redox-active organic molecules. Electronic descriptors extracted from DFT calculations served as inputs for various machine learning models, whose performances were evaluated using R2, MAE, and RMSE metrics. The results demonstrated that the SVR model achieved the highest predictive accuracy, with a coefficient of determination (R2) of 0.974 and the lowest error rate, while the Stacking model exhibited closely comparable and stable performance. Correlation analysis, feature importance, and SHAP values revealed that electron transfer-related descriptors, particularly electron affinity, play the most significant role in predicting redox potentials. The consistency between ML results and quantum chemical concepts indicates that the proposed framework achieves high predictive accuracy alongside robust interpretability. This study highlights that the integration of DFT calculations and machine learning can provide an efficient approach for the rapid screening and targeted design of redox-active organic molecules with optimized electrochemical properties.https://jmrph.khu.ac.ir/article_11774_73a32464b3cb3ac9b59c7eb5b4356730.pdfKharazmi UniversityJournal Of Modern Research Physics2588-492111120260823Simulation of Effective Dirac Dynamic via Quantum Walks on Digital Quantum ComputersSimulation of Effective Dirac Dynamic via Quantum Walks on Digital Quantum Computers13231187410.22034/jmrph.2026.122919.0FAZeynabKiamehrTafresh UniversityDepartment of Physics, Faculty of Basic Sciences, Tafresh University, Tafresh, Iran0000-0002-1649-0564Journal Article20260209Quantum field theory (QFT) forms the fundamental basis of many areas in physics and is indispensable for our understanding of the universe. This research shows that simulating QFT using digital quantum computers is not merely a numerical computation, but rather a foundational problem in physical modeling that simultaneously depends on the theoretical structure of QFT, the choice of quantum algorithm, the definition of fidelity metrics, and the limitations of existing hardware. Since the equations of quantum field theories are generally complex and analytically intractable, employing quantum walks as a computational framework provides an alternative approach that enables the exploration of these theories in regimes inaccessible to classical methods. Within this framework, digital quantum walks serve as a natural algorithmic implementation for simulating the dynamics of quantum fields, as their discrete structure aligns well with the intrinsic architecture of NISQ-era quantum processors and, in the continuum limit, converges toward fundamental equations such as the Dirac equation. However, simulations and implementations on IBM’s superconducting quantum hardware reveal a high sensitivity to noise: even within non-interacting models, noise effects can fundamentally distort the interference patterns of the probability distributions. Consequently, defining a precise quantitative metric to assess simulation validity becomes crucial. The adoption of Hellinger fidelity as the central measure enables a systematic comparison between the probability distributions obtained from ideal simulations, noisy models, and experimental data, clearly indicating the extent to which the simulated dynamics can be interpreted as a valid representation of QFT.Quantum field theory (QFT) forms the fundamental basis of many areas in physics and is indispensable for our understanding of the universe. This research shows that simulating QFT using digital quantum computers is not merely a numerical computation, but rather a foundational problem in physical modeling that simultaneously depends on the theoretical structure of QFT, the choice of quantum algorithm, the definition of fidelity metrics, and the limitations of existing hardware. Since the equations of quantum field theories are generally complex and analytically intractable, employing quantum walks as a computational framework provides an alternative approach that enables the exploration of these theories in regimes inaccessible to classical methods. Within this framework, digital quantum walks serve as a natural algorithmic implementation for simulating the dynamics of quantum fields, as their discrete structure aligns well with the intrinsic architecture of NISQ-era quantum processors and, in the continuum limit, converges toward fundamental equations such as the Dirac equation. However, simulations and implementations on IBM’s superconducting quantum hardware reveal a high sensitivity to noise: even within non-interacting models, noise effects can fundamentally distort the interference patterns of the probability distributions. Consequently, defining a precise quantitative metric to assess simulation validity becomes crucial. The adoption of Hellinger fidelity as the central measure enables a systematic comparison between the probability distributions obtained from ideal simulations, noisy models, and experimental data, clearly indicating the extent to which the simulated dynamics can be interpreted as a valid representation of QFT.https://jmrph.khu.ac.ir/article_11874_786ab8c4d7ee758f80d57e65582e609d.pdfKharazmi UniversityJournal Of Modern Research Physics2588-492111120260823Improving the quality of images with focus distortion using convolutional neural networksImproving the quality of images with focus distortion using convolutional neural networks24311187610.22034/jmrph.2026.122923.0FAFatemehFallahiZahraFallahiMohammadRezaJafarfardBabakZareJournal Article20260224Defocus distortion is one of the primary factors that degrades the quality of microscopic images, leading to reduced sharpness, loss of fine details, and decreased accuracy in image analysis. Therefore, the development of effective methods for defocus correction is of considerable importance in digital microscopy. In this study, a convolutional autoencoder neural network, consisting of convolutional layers, a compressed bottleneck, and transposed convolutional layers, was designed and trained to correct defocus distortion in digital microscopic images. To this end, a dataset comprising reference images and corresponding defocused images generated using the Fresnel diffraction model was constructed and employed for training and evaluation. The performance of the proposed model was assessed using the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The proposed model achieved an MSE of 0.0008, a PSNR of 30.97 dB, and an SSIM of 0.9839, indicating accurate image reconstruction and excellent structural similarity between the restored and reference images. These results demonstrate that the proposed approach provides an effective solution for defocus correction, improving the quality and sharpness of digital microscopic images.Defocus distortion is one of the primary factors that degrades the quality of microscopic images, leading to reduced sharpness, loss of fine details, and decreased accuracy in image analysis. Therefore, the development of effective methods for defocus correction is of considerable importance in digital microscopy. In this study, a convolutional autoencoder neural network, consisting of convolutional layers, a compressed bottleneck, and transposed convolutional layers, was designed and trained to correct defocus distortion in digital microscopic images. To this end, a dataset comprising reference images and corresponding defocused images generated using the Fresnel diffraction model was constructed and employed for training and evaluation. The performance of the proposed model was assessed using the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The proposed model achieved an MSE of 0.0008, a PSNR of 30.97 dB, and an SSIM of 0.9839, indicating accurate image reconstruction and excellent structural similarity between the restored and reference images. These results demonstrate that the proposed approach provides an effective solution for defocus correction, improving the quality and sharpness of digital microscopic images.https://jmrph.khu.ac.ir/article_11876_a8e5a72192378802318bf51063153729.pdf