Li, J., Yang, L., Wan, L., Hu, W., & Yang, J. (2024). Machine Learning K-Means Clustering in Interpolative Separable Density Fitting Algorithm: Advancing Accurate and Efficient Cubic-Scaling Density Functional Perturbation Theory Calculations within Plane Waves. The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 128(10), 1913. https://doi.org/10.1021/acs.jpca.3c07159
Citace podle Chicago (17th ed.)Li, Jielan, Liu Yang, Lingyun Wan, Wei Hu, a Jinlong Yang. "Machine Learning K-Means Clustering in Interpolative Separable Density Fitting Algorithm: Advancing Accurate and Efficient Cubic-Scaling Density Functional Perturbation Theory Calculations Within Plane Waves." The Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, & General Theory 128, no. 10 (2024): 1913. https://doi.org/10.1021/acs.jpca.3c07159.
Citace podle MLA (9th ed.)Li, Jielan, et al. "Machine Learning K-Means Clustering in Interpolative Separable Density Fitting Algorithm: Advancing Accurate and Efficient Cubic-Scaling Density Functional Perturbation Theory Calculations Within Plane Waves." The Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, & General Theory, vol. 128, no. 10, 2024, p. 1913, https://doi.org/10.1021/acs.jpca.3c07159.