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<p>Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant <i>O</i>(3) irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to f orbital matrix interaction blocks. We demonstrate the model’s accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening.</p>

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Equivariant electronic Hamiltonian prediction with many-body message passing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月11日

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  • Chen Qian,
  • Valdas Vitartas,
  • James R. Kermode,
  • Reinhard J. Maurer

摘要

Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant O(3) irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to f orbital matrix interaction blocks. We demonstrate the model’s accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening.