mlsanityData to 14 Oct 2026

Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids

arXiv 2210.16190 ↗First posted 28 Oct 2022physics.comp-ph cond-mat.mtrl-sci cs.LGcited by 8

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Abstract (arXiv)

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. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 14 of its 85 references that are in the map. First posted on arXiv 28 Oct 2022.

Builds on

14 of 85 references.

References · 14

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2 more
  1. 2021Equivariant representations for molecular Hamiltonians and N-center atomic-scale propertiescited by 19
  2. 2021Heterogeneous relational message passing networks for molecular dynamics simulationscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 4; 2025: 2; 2026: 1.

8 papers.

  1. 2025Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systemscited by 14
  2. 2025Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Framescited by 6
  3. 2024Universal Machine Learning Kohn-Sham Hamiltonian for Materialscited by 3
  4. 2022General time-reversal equivariant neural network potential for magnetic materialscited by 2
  5. 2024nabla2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentialscited by 2
  6. 2024Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistorscited by 1
  7. 2024Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approachcited by 1
  8. 2026Equivariant Flow Matching for Electron Density Predictioncited by 0