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Page 4 of 23 Li et al. J. Mater. Inf. 2025, 5, 43 https://dx.doi.org/10.20517/jmi.2025.17
Figure 1. An overview of deep IAP development and outlook on associated data, models, and optimization strategies. IAP: Learning
interatomic potential.
Figure 2. (A) Structural representations: integrating structural features into GNNs, including distance only, both distance and angles, and
all distance, angles, and dihedral angles. The concepts of different structural representations within the context of energy (scalar) and
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force (vector) prediction . The d, α, and Φ represent the bond length, bond angle, and dihedral angle, respectively; (B) Schematic
diagram of massage passing and aggregation in equivariant representations of crystalline structures under a rotation operation. GNNs:
Graph neural networks.
Heisenberg exchange and spin-lattice couplings through equivariant message passing, thereby capturing
multi-body and higher-order spin interactions with high fidelity.
