fig2

Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks

Figure 2. Replacing MLPs in different parts of the ML potential Allegro[10] with KANs employing various basis functions. Zi stands for the chemical species of atom i. $$ \overrightarrow{r_{i j}} $$ stands for the relative displacement vector from atom i to atom j. Substituting MLPs with KANs generally enhances prediction accuracy. Specifically, replacing MLPs in the output block of the Allegro model results in higher prediction accuracy and shorter training time than using MLP, and higher inference speed and higher computation resource efficiency than using KANs throughout the entire model. MLPs: Multi-layer perceptrons; KANs: Kolmogorov-Arnold Networks; ML: Machine learning.

Journal of Materials Informatics
ISSN 2770-372X (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/