Publications

  1. G. M. Jones*#, V. K. Prasad, U. Fekl, H.-A. Jacobsen, Parametrized Quantum Circuit Learning for Quantum Chemical Applications, submitted.
  2. G. M. Jones*, K. D. Vogiatzis, Capturing Electron Correlation with Machine Learning through a Data-Driven CASPT2 Framework , accepted. (Preprint)
  3. G. M. Jones*#, H.-A. Jacobsen, Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing, accepted.
  4. H.-A. Jacobsen, G. M. Jones, M. Amy, H. Müller, U. Stege, A. Choquette,Distributed Quantum Computing Applications, Challenges and Opportunities, accepted.
  5. A. G. Sylvanus*, G. M. Jones*, R. Custelcean, K. D. Vogiatzis, In Silico Screening of CO2-Dipeptide Interactions for Bioinspired Carbon Capture, ChemPhysChem, 2024, e202400498.
  6. G. M. Jones*#, H.-A. Jacobsen, Distributed Quantum Computing for Chemical Applications, In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 2024, Vol. 2, 155-160. (Preprint)
  7. G. M. Jones*, R. R. Li, A. E. DePrince III, K. D. Vogiatzis, Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory, J. Phys. Chem. Lett., 2023, 14, 28, 6377-6385. (Preprint)
  8. G. M. Jones*, B. Story, V. Maroulas, K. D. Vogiatzis, Molecular Representations for Machine Learning, ACS In Focus; American Chemical Society: Washington DC, 2023.
  9. G. M. Jones*, B. A. Smith*, J. K. Kirkland, K. D. Vogiatzis, Data-Driven Ligand Field Exploration of Fe(IV)-oxo Sites for C-H Activation, Inorg. Chem. Front., 2023, 10, 1062-1075. 
  10. G. M. Jones*, P. D. V. S. Pathirage, K. D. Vogiatzis, Data-driven Acceleration of Coupled-Cluster Theory and Perturbation Theory Methods, in: “Quantum Chemistry in the Age of Machine Learning”, 2022, Editor: Pavlo Dral, Elsevier, pp. 509-529.

* denotes first author publications

# denotes corresponding author publications