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Found 7 papers, 3 papers with code
Universal Machine Learning Kohn-Sham Hamiltonian for Materials
By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible.
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.
Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations.
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.
Edge-based Tensor prediction via graph neural networks
Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT).
Complex Spin Hamiltonian Represented by Artificial Neural Network
The effective spin Hamiltonian method is widely adopted to simulate and understand the behavior of magnetism.
