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Found 3 papers, 2 papers with code
Capturing long-range interaction with reciprocal space neural network
The structure information in real space is firstly transformed into reciprocal space and then encoded into a reciprocal space potential or a global descriptor with full atomic interactions.
General time-reversal equivariant neural network potential for magnetic materials
This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments.
Spin-Dependent Graph Neural Network Potential for Magnetic Materials
The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals.
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