Jijila, B., Ezhilarasi, S. S., & Nirmala, V. (2025). Machine-learned density functional based quantum chemical computations for ethane: performance of Deep Mind 21 on potential energy surface and molecular properties. Journal of Molecular Modeling, 31(10), 1-19. https://doi.org/10.1007/s00894-025-06451-3

JIJILA, B., EZHILARASI, S. Susannal und NIRMALA, V., 2025. Machine-learned density functional based quantum chemical computations for ethane: performance of Deep Mind 21 on potential energy surface and molecular properties. Journal of Molecular Modeling. 1 Oktober 2025. Vol. 31, no. 10, p. 1-19. DOI 10.1007/s00894-025-06451-3.

Jijila, B., S. S. Ezhilarasi, und V. Nirmala. „Machine-Learned Density Functional Based Quantum Chemical Computations for Ethane: Performance of Deep Mind 21 on Potential Energy Surface and Molecular Properties.“. Journal of Molecular Modeling, Bd. 31, Nr. 10, Oktober 2025, S. 1-19, https://doi.org/10.1007/s00894-025-06451-3.

Jijila, B./Ezhilarasi, S. Susannal/Nirmala, V.: Machine-learned density functional based quantum chemical computations for ethane: performance of Deep Mind 21 on potential energy surface and molecular properties., Journal of Molecular Modeling 2025, 1-19.

Jijila, B., Ezhilarasi, S.S. und Nirmala, V. (2025), „Machine-learned density functional based quantum chemical computations for ethane: performance of Deep Mind 21 on potential energy surface and molecular properties.“, Journal of Molecular Modeling, Vol. 31 No. 10, S. 1-19.