Neural network approaches for quantum energy level prediction

Khalili Golmankhaneh A., Pasechnik R., Jørgensen P. E. T.

Soft Computing, 2026 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s00500-026-11300-3
  • Dergi Adı: Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, zbMATH
  • Anahtar Kelimeler: Artificial neural networks (ANNs), Harmonic oscillator, Hydrogen atom, Machine learning in quantum physics, Particle in a box, Quantum energy Levels, Schrödinger equation
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Artificial neural networks (ANNs) are employed to predict the energy levels of quantum systems, including the hydrogen atom, the harmonic oscillator, and the particle in a box, using various activation functions. The results demonstrate that the choice of activation function significantly influences prediction accuracy. This data-driven framework provides a powerful alternative to explicitly solving the Schrödinger equation, enabling direct estimation of quantum energy spectra from experimental data. The proposed method offers an efficient and flexible tool for the analysis of quantum systems in laboratory settings.