First-Principles Investigation of Structural, Electronic, X-ray Spectroscopic, and Optical Properties of CaRhH₃
DOI:
https://doi.org/10.54536/jir.v4i2.7586Keywords:
Artificial Intelligence, Carhh₃, Castep, Density Functional Theory, Electronic Structure, achine Learning, Metal Hydrides, Optical PropertiesAbstract
The CaRhH₃ compound has recently attracted attention due to its potential application in hydrogen energy systems; however, its electronic and optical properties remain insufficiently explored. In this work, density functional theory (DFT) calculations within the CASTEP framework are employed to systematically investigate the structural, electronic, and optical characteristics of CaRhH₃. Structural stability is confirmed through full geometry optimization and simulated powder X-ray diffraction analysis. The calculated electronic band structure indicates metallic behavior, primarily governed by Rh-d states near the Fermi level. To enhance the reliability of the electronic structure analysis, Gaussian Process Regression (GPR)-based machine learning is applied to the density of states (DOS), enabling smooth profile generation and uncertainty quantification. This approach significantly improves peak identification and interpretation of electronic features. Furthermore, element-specific X-ray absorption and emission spectrum provide detailed insight into the contributions of Ca and Rh to the occupied and unoccupied states. Optical properties, including absorption and reflectivity, reveal strong interaction with electromagnetic radiation, particularly in the ultraviolet region. This study highlights the methodological novelty of integrating DFT with machine learning techniques for advanced electronic structure analysis. The findings not only improve the fundamental understanding of CaRhH₃ but also demonstrate its potential suitability for hydrogen energy applications, offering both scientific and technological significance.
References
Adedoja, O. S., Sadiku, E. R., & Hamam, Y. (2023). Density functional theory investigation of the energy storage potential of graphene‐polypyrrole nanocomposites as high‐performance electrode for Zn‐ion batteries. Polymer Engineering & Science, 63(10), 3398-3410.
Afrasiabi, A. (2023). Novel AI-assisted computational solutions for GPR data interpretation and electromagnetic data fusion to detect buried utilities University of Birmingham].
Akbar, Z., Jiang, Y., Webb, R., Klotzsche, A., Zhu, Y., Anwar, A., & Rehman, M. M. (2025). Machine learning-based dual-parameter inversion for estimating snowpack liquid water content and density using common offset GPR data. Science China Earth Sciences, 1-18.
Aldridge, S., & Downs, A. J. (2001). Hydrides of the main-group metals: New variations on an old theme. Chemical Reviews, 101(11), 3305-3366.
Arfi, A. M., Nabi, M., Mia, M. H., Alsalmi, O., Uddin, M. A., & Hasan, M. Z. (2025). First-principles insights into thermo-mechanical, electronic, and optical properties of Hf 2 AC (A= Cl, Br) MAX phases through A-site halogen tuning. RSC advances, 15(50), 42698-42719.
Basri, K. N., Zabidi, N. A., Abu Kassim, H., & Rosli, A. N. (2015). Density Functional Theory (DFT) Calculation of Band Structure of Kesterite. Advanced Materials Research, 1107, 491-495.
Cuevas, F., Amdisen, M. B., Baricco, M., Buckley, C. E., Cho, Y. W., De Jongh, P., De Kort, L. M., Grinderslev, J. B., Gulino, V., & Hauback, B. C. (2022). Metallic and complex hydride-based electrochemical storage of energy. Progress in Energy, 4(3), 032001.
Dewangan, S. K., Mohan, M., Kumar, V., Sharma, A., & Ahn, B. (2022). A comprehensive review of the prospects for future hydrogen storage in materials‐application and outstanding issues. International Journal of Energy Research, 46(12), 16150-16177.
El-Adawy, M., Zayed, M. E., Shboul, B., Ashraf, W. M., & Nemitallah, M. A. (2024). Performance improvement of compression ignition engine fueled by second generation biodiesel fuel blends enriched with ZnO nanoparticles: Experimental study and Gaussian process regression AI modeling. Process Safety and Environmental Protection, 190, 1372-1385.
El Kharbachi, A., Dematteis, E., Shinzato, K., Stevenson, S., Bannenberg, L., Heere, M., Zlotea, C., Szilágyi, P. Á., Bonnet, J.-P., & Grochala, W. (2020). Metal hydrides and related materials. Energy carriers for novel hydrogen and electrochemical storage. In (Vol. 124, pp. 7599-7607): ACS Publications.
Giannakis, I., Giannopoulos, A., & Warren, C. (2019). A machine learning-based fast-forward solver for ground penetrating radar with application to full-waveform inversion. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4417-4426.
Han, C., Kundu, B. K., Liang, Y., & Sun, Y. (2024). Near‐infrared light‐driven photocatalysis with an emphasis on two‐photon excitation: concepts, materials, and applications. Advanced Materials, 36(5), 2307759.
Hasnip, P. J., Refson, K., Probert, M. I., Yates, J. R., Clark, S. J., & Pickard, C. J. (2014). Density functional theory in the solid state. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2011).
Jin, H., Kim, Y.-G., Jin, Z., Rushchitc, A. A., & Al-Shati, A. S. (2022). Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models. Energy Reports, 8, 13979-13996.
Kerr, B. V., King, H. J., Garibello, C. F., Dissanayake, P. R., Simonov, A. N., Johannessen, B., Eldridge, D. S., & Hocking, R. K. (2022). Characterization of energy materials with X-ray absorption spectroscopy─ advantages, challenges, and opportunities. Energy & Fuels, 36(5), 2369-2389.
Khan, L. U., Khan, Z. U., AlZubi, R. I., Umer, M. A., Juwhari, H. K., Harfouche, M., & Brito, H. F. (2024). Tracking coordination environment and optoelectronic structure of Eu3+ and Sm3+ sites via X-ray absorption spectroscopy and X-ray excited optical luminescence. Materials Today: Proceedings.
Khan, L. U., Khan, Z. U., Blois, L., Tabassam, L., Brito, H. F., & Figueroa, S. J. (2023). Strategy to probe the local atomic structure of luminescent rare earth complexes by X-ray absorption near-edge spectroscopy simulation using a machine learning-based PyFitIt approach. Inorganic Chemistry, 62(6), 2738-2750.
Li, S., Han, Z., Deng, J., Duanmu, P., Liu, Q., Liu, J., Zhang, H., Xing, D., Liu, C., & Yang, Z. (2025). New insights into adsorption mechanism of sodium oleate on the cassiterite (110) surface and its stability behavior under temperature: An approach combining quantum and molecular mechanics. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 138442.
Lin, Z., Jiang, X., Kang, L., Gong, P., Luo, S., & Lee, M.-H. (2014). First-principles materials applications and design of nonlinear optical crystals. Journal of Physics D: Applied Physics, 47(25), 253001.
Liu, Y., Su, X., Ding, J., Zhou, J., Liu, Z., Wei, X., Yang, H. B., & Liu, B. (2024). Progress and challenges in structural, in situ and operando characterization of single-atom catalysts by X-ray based synchrotron radiation techniques. Chemical Society Reviews.
McGrady, G. S., & Guilera, G. (2003). The multifarious world of transition metal hydrides. Chemical Society Reviews, 32(6), 383-392.
Oganov, A. R., Pickard, C. J., Zhu, Q., & Needs, R. J. (2019). Structure prediction drives materials discovery. Nature Reviews Materials, 4(5), 331-348.
Pan, S., Wang, T., Jin, K., & Cai, X. (2022). Understanding and designing metal matrix nanocomposites with high electrical conductivity: A review. Journal of Materials Science, 57(12), 6487-6523.
Pearson, R. G. (1985). The transition-metal-hydrogen bond. Chemical Reviews, 85(1), 41-49.
Rahman, B. S. M. Study of Structure, Band, and Density of States Properties of TlPbCl₃ with DFT Based on First-Principles Calculations Department of Physics, Bangabandhu Sheikh Mujibur Rahman Science and …].
Saad, A. M. H. Next-Generation Solar Cells: Advancements in Materials, Architectures, and System Integration for A Sustainable Energy Future.
Saad, A. M. H., & Myat, A. (2025). Role of Solar Energy in Achieving Global Net-Zero Targets: Policy and Technological Perspective. AMERICAN JOURNAL OF ENERGY AND NATURAL RESOURCES Учредители: E-palli, 4(1), 1-8.
Schneemann, A., White, J. L., Kang, S., Jeong, S., Wan, L. F., Cho, E. S., Heo, T. W., Prendergast, D., Urban, J. J., & Wood, B. C. (2018). Nanostructured metal hydrides for hydrogen storage. Chemical Reviews, 118(22), 10775-10839.
Shu, H., Wang, F., Ren, K., & Guo, J. (2025). Strain-tunable optoelectronic and photocatalytic properties of 2D GaN/MoSi 2 P 4 heterobilayers: potential optoelectronic/photocatalytic materials. Nanoscale, 17(7), 3900-3909.
Stahl, K., Graziadei, A., Dau, T., Brock, O., & Rappsilber, J. (2023). Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning. Nature Biotechnology, 41(12), 1810-1819.
Troullier, N., & Martins, J. L. (1991). Efficient pseudopotentials for plane-wave calculations. Physical review B, 43(3), 1993.
Vanguri, R. S., Luo, J., Aukerman, A. T., Egger, J. V., Fong, C. J., Horvat, N., Pagano, A., Araujo-Filho, J. d. A. B., Geneslaw, L., & Rizvi, H. (2022). Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer. Nature cancer, 3(10), 1151-1164.
Xu, Y., Yang, X., Li, Y., Zhao, Y., Shu, X., Zhang, G., Yang, T., Liu, Y., Wu, P., & Ding, Z. (2024). Rare-Earth Metal-Based Materials for Hydrogen Storage: Progress, Challenges, and Future Perspectives. Nanomaterials, 14(20), 1671.
Yuan, Y., Patel, R. K., Banik, S., Reta, T. B., Bisht, R. S., Fong, D. D., Sankaranarayanan, S. K., & Ramanathan, S. (2024). Proton conducting neuromorphic materials and devices. Chemical Reviews, 124(16), 9733-9784.
Zhang, W., Suo, L., Wen, J., Ji, X., Huang, J., Zhao, Y., Qian, Y., Suo, C., Zhang, H., & Ma, Y. (2025). Layer-by-layer stacked nano-CuZn5 for conductivity multi-enhancement and surface engineering of anode current collector in dendrite-free sodium metal batteries. Energy, 138809.
Zhang, X., Tian, X.-L., Qin, Y., Qiao, J., Pan, F., Wu, N., Wang, C., Zhao, S., Liu, W., & Cui, J. (2023). Conductive metal–organic frameworks with tunable dielectric properties for boosting electromagnetic wave absorption. ACS nano, 17(13), 12510-12518.
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