Quantized Atomic Energies Emerge from Classical Subatomic Particle Simulations: Discovery of Quantum Effects enclosed in Classical Mechanics explaining Atomic Spectral Lines

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Shanmuga Sundaram Venkatesan

Abstract

Classical subatomic particle simulations were applied to protons, neutrons, and electrons to simulate an individual atom using pseudo-potentials to simulate the nucleus and electrons distributed around the nucleus. We applied molecular dynamics algorithms to subatomic particles to simulate complete atoms of Hydrogen, Carbon, and Uranium, as reported in our previous studies. Investigation of the Carbon atom's energy as a function of time reveals quantised, discrete energy values rather than the continuous energies expected in classical simulations. Changes in an atom's total energy were found to occur in discrete steps as it transitionsto higher/lower energy states, and the energy values are observed to be quantised rather than continuous. Further analysis examined individual electron energies to determine how electrons behave during an atom's quantum energy shifts. Only one electron's energy profile had undergone a step change, leaving the other five electrons unaffected in a Carbon atom. To study the energy quantisation property in other atoms, the Fluorine, Magnesium, and Chlorine atoms were considered and found to exhibit similar quantised, discrete energy properties. The energy quantisation property of an individual atom was already accounted for in classical mechanics/models. This study uses classicalmechanics simulations of subatomic particles to investigate why atomic energies are quantised in nature, as observed in discrete lines in atomic emission/absorption spectra.

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Shanmuga Sundaram Venkatesan , Tran., “Quantized Atomic Energies Emerge from Classical Subatomic Particle Simulations: Discovery of Quantum Effects enclosed in Classical Mechanics explaining Atomic Spectral Lines”, IJAP, vol. 6, no. 2, pp. 1–6, Oct. 2026, doi: 10.54105/ijap.B1076.06021026.
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References

Ferrara, C.; Giarrusso, M.; Leone, F. Experimental atomic data of spectral lines--I. Cs, Ba, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Re, and Os in the 370--1000 nm interval. Monthly Notices of the Royal Astronomical Society 2024, 527, 4440-4466.

DOI: https://doi.org/10.1093/mnras/stad3230

Kramida, A. Evaluation of uncertainties in atomic data on spectral lines and transition probabilities. The European Physical Journal D 2024, 78, 36. DOI: https://doi.org/10.1140/epjd/s10053-024-00820-y

Ni, Q.-H.; Wang, R.; Xie, T.; Zhang, J.; Hill, C.; Yurchenko, S. N.; Tennyson, J. ExoAtom: A Database of Atomic Spectra in ExoMol Format. RAS Techniques and Instruments 2025, rzaf065. DOI: https://doi.org/10.1093/rasti/rzaf065

Ralchenko, Y.; Kramida, A. Development of NIST atomic databases and online tools. Atoms 2020, 8, 56.

DOI: https://doi.org/10.3390/atoms8030056

Goldenstein, C. S.; Miller, V. A.; Mitchell, Spearrin, R.; Strand, C. L. SpectraPlot.com: Integrated spectroscopic modelling of atomic and molecular gases. Journal of Quantitative Spectroscopy and Radiative Transfer 2017, 200, 249-257. DOI: https://doi.org/10.1016/j.jqsrt.2017.06.007

Jacquinet-Husson, N.; Armante, R.; Scott, N.; Chédin, A.; Crépeau, L.; Boutammine, C.; Bouhdaoui, A.; Crevoisier, C.; Capelle, V.; Boonne, C.; others. The 2015 edition of the GEISA spectroscopic database. Journal of Molecular Spectroscopy 2016, 327, 31-72.

DOI: https://doi.org/10.1016/j.jms.2016.06.007

Kramida, A.; Ralchenko, Y.; Reader, J.; Team, N. A. NIST Atomic Spectra Database (ver. 5.12). National Institute of Standards and Technology, Gaithersburg, MD. 2024. DOI: https://doi.org/10.18434/T4W30F

Pinke, D. D.; Õsz, K.; Lente, G. The origin of the postulates in the Bohr model of the hydrogen atom. ChemTexts 2025, 11, 11.

DOI: https://doi.org/10.1007/s40828-025-00208-4

Eastwood, M. A. Heisenberg's uncertainty principle. QJM: An International Journal of Medicine 2017, 110, 335-336.

DOI: https://doi.org/10.1093/qjmed/hcw193

Karam, R. Schrödinger's original struggles with a complex wave function. American Journal of Physics 2020, 88, 433-438.

DOI: https://doi.org/10.1119/10.0000852

Teller, P. An interpretive introduction to quantum field theory. Princeton University Press. 2020. DOI: https://doi.org/10.1515/9780691216294

Davis, E.; Bentsen, G.; Schleier-Smith, M. Approaching the Heisenberg limit without single-particle detection. Physical Review Letters 2016, 116, 053601. DOI: https://doi.org/10.1103/PhysRevLett.116.053601

West, A. R. Solid-state chemistry and its applications. John Wiley & Sons, 2022. DOI: https://doi.org/10.1002/crat.2170200233

Preskill, J. Quantum computing in the NISQ era and beyond. Quantum 2018, 2, 79. DOI: https://doi.org/10.22331/q-2018-08-06-79

Sergienko, A. V. Quantum communications and cryptography. CRC Press, 2018. DOI: https://doi.org/10.1201/9781315221120

Frenkel, D.; Smit, B. Understanding molecular simulation: from algorithms to applications. Elsevier, 2023.

DOI: https://doi.org/10.1063/1.881812

Eastman, P.; Swails, J.; Chodera, J. D.; McGibbon, R. T.; Zhao, Y.; Beauchamp, K. A.; Wang, L.-P.; Simmonett, A. C.; Harrigan, M. P.; Stern, C. D.; others. OpenMM 7: Rapid development of high-performance algorithms for molecular dynamics. PLoS Computational Biology 2017, 13, e1005659. DOI: https://doi.org/10.1371/journal.pcbi.1005659

Hollingsworth, S. A.; Dror, R. O. Molecular dynamics simulation for all. Neuron 2018, 99, 1129-1143.

DOI: https://doi.org/10.1016/j.neuron.2018.08.011

Keldysh, L. V. Selected Papers of Leonid V. Keldysh. World Scientific, 2024; pp. 155-158. DOI: https://doi.org/10.1142/9789811279461_0024

Venkatesan, S. Subatomic Particle Simulations using Monte Carlo and Molecular Dynamics Algorithms to Simulate Stable Atoms and Model Electronic Structures. Preprints 2025. DOI: https://doi.org/10.20944/preprints202311.1107.v3

Venkatesan, S. S. Subatomic Particle Simulations using Monte Carlo and Molecular Dynamics Algorithms to Simulate Stable Atom and Model Electronic Structures. Innovative Journal of Applied Science 2025, 2, 20. DOI: https://doi.org/10.70844/ijas.2025.1.20

Venkatesan, S. Subatomic Particle Simulations using Monte Carlo and Molecular Dynamics Algorithms to Simulate Stable Atom. Eliva Press, 2025. URL: https://www.elivapress.com/en/book/book-7213394112

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