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Fast and Accurate Excitation Energies from Density Matrix Renormalization Group Calculations Improved by Machine Learning

  • Pavlo Golub
  • Libor Veis
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Abstract

An estimation of optoelectronic properties is a crucial necessity and an ongoing challenge in modern functional material science. This involves accurate accessing of ground and excited states in systems with complex electronic structure that are often too computationally expensive for accurate many-body methods. Building on our previous work on ground states [J. Phys. Chem. Lett. 2025, 16, 3295-3301], we present an efficient and cost-effective approach for evaluating electronic excitations in functional materials, combining the density matrix renormalization group method as a complete active space solver with machine learning techniques. We demonstrate its performance on π-electron-correlated systems, namely polycyclic aromatic compounds. The transferability and effectiveness of the derived machine learning model are demonstrated on a number of challenging examples with up to 34 π-electrons.

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Cite

BibTeXRIS
BibTeX
@misc{golub2026fast,
  title = {{Fast and Accurate Excitation Energies from Density Matrix Renormalization Group Calculations Improved by Machine Learning}},
  author = {Pavlo Golub and Libor Veis},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.12616},
  eprint = {2609.12616},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.12616},
}
RIS
TY  - UNPB
TI  - Fast and Accurate Excitation Energies from Density Matrix Renormalization Group Calculations Improved by Machine Learning
AU  - Pavlo Golub
AU  - Libor Veis
PY  - 2026
DA  - 2026/09/11
PB  - arXiv
DO  - 10.48550/arxiv.2609.12616
UR  - https://arxiv.org/abs/2609.12616
AB  - An estimation of optoelectronic properties is a crucial necessity and an ongoing challenge in modern functional material science. This involves accurate accessing of ground and excited states in systems with complex electronic structure that are often too computationally expensive for accurate many-body methods. Building on our previous work on ground states [J. Phys. Chem. Lett. 2025, 16, 3295-3301], we present an efficient and cost-effective approach for evaluating electronic excitations in functional materials, combining the density matrix renormalization group method as a complete active space solver with machine learning techniques. We demonstrate its performance on π-electron-correlated systems, namely polycyclic aromatic compounds. The transferability and effectiveness of the derived machine learning model are demonstrated on a number of challenging examples with up to 34 π-electrons.
ER  -