Dr. Sigbjørn Løland Bore

Researcher @ Hylleraas Centre
Young researcher talent
YoungCAS fellow
University of Oslo
Department of Chemistry
Sem Sælands vei 26
0371 Oslo

Telephone number (NO): (+47) 928 81 237
University E-mail: s.l.bore(at)kjemi.uio.no
Personal E-mail: Sigbjorn.Loland.Bore(at)gmail.com
Google scholar


  1. Harnessing Hydrogen by Optimized Ion-Conductive Metal-Organic Frameworks Researcher Project for Young Talents (FRIPRO) (2023-2027)
  2. Can ice be described from first principles?
    Young CAS programme (2023-2025)

Publications and manuscripts:

  1. Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics
    M. Carrer, H.M. Cezar, SLB, M. Ledum, M. Cascella
    Under review (2024) [ChemRxiv]
  2. Realistic phase diagram of water from “first principles” data-driven quantum simulations
    SLB, F. Paesani
    Nat. Commun. 14 3349 (2023) [ChemRxiv]
  3. DeePMD-kit v2: A software package for Deep Potential models
    J. Zeng, ..., SLB, et al.
    J. Chem. Phys. 159, 054801 (2023) [arXiv]
  4. Many-body Potential for Simulating the Self-Assembly of Polymer-Grafted Nanoparticles in a Polymer Matrix
    Y. Zhou, SLB, A.R. Tao, F. Paesani, Gaurav Arya
    Npj Comput. Mater. 9, 224 (2023) [ChemRxiv]
  5. Soft Matter under Pressure: Pushing Particle-Field Molecular Dynamics to the Isobaric Ensemble
    S. Sen, M. Ledum, SLB, M. Cascella
    J. Chem. Inf. Model. 63, 2207–2217 (2023) [ChemRxiv]
  6. On the equivalence of the hybrid particle-field and Gaussian core models
    M. Ledum, S. Sen, SLB, M Cascella
    J. Chem. Phys., 158 194902 (2023) [arXiv]
  7. A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or learning the underlying physics?
    Y. Zhai, A. Caruso, SLB, Z. Luo, F. Paesani
    J. Chem. Phys. 158, 084111 (2023) [ChemRxiv] [Scilight]
  8. HylleraasMD: A Domain Decomposition-Based Hybrid Particle-Field Software for Multi-Scale Simulations of Soft Matter
    M. Ledum, S. Sen, X. Li, M. Carrer, Yu Feng, M. Cascella, SLB
    J. Chem. Theory Comput. 19, 2939 (2023) [ChemRxiv]
  9. HylleraasMD: Massively parallel hybrid particle-field molecular dynamics in Python
    M. Ledum, S. Sen, X. Li, M. Carrer, M. Cascella, SLB
    J. Open Source Softw. 8 4149 (2023)

  10. Data-Driven Many-Body Potential Energy Functions for Generic Molecules: Linear Alkanes as a Proof-of-Concept Application
    E.F. Bull-Vulpe, M. Riera, SLB, F. Paesani
    J. Chem. Theory Comput. 19, 4494 (2022) [ChemRxiv]
  11. Phase diagram of the TIP4P/Ice water model by enhanced sampling simulations
    SLB, P.M. Piaggi, R. Car, F. Paesani
    J. Chem. Phys. 157, 054504 (2022) [ChemRxiv]
  12. Aggregation of Lipid A Variants: a Hybrid Particle-Field Model
    A. De Nicola, T.A. Soares, D.E.S. Santos, SLB, G.J.A. Sevink, M. Cascella, G. Milano
    Biochim. Biophys. Acta 1865, 129570 (2021) [ChemRxiv]
  13. Dispersion state phase diagram of citrate-coated metallic nanoparticles in saline solutions
    S. Franco-Ulloa, G. Tatulli, SLB, M. Moglianetti, P. Pompa, M. Cascella, M. De Vivo
    Nat. Commun. 11, 5422 (2020) [ChemRxiv]
  14. Hamiltonian and Alias-Free Hybrid Particle-Field Molecular Dynamics
    SLB, M. Cascella
    J. Chem. Phys. 153, 094106 (2020) [arXiv]
  15. Can polarity-inverted surfactants self-assemble in nonpolar solvents?
    M. Carrer, T. Skrbic, SLB, G. Milano, M. Cascella, A. Giacometti
    J. Phys. Chem. B 124, 6448-6458 (2020) [ChemRxiv]
  16. Supramolecular Packing Drives Morphological Transition of Charged Surfactant Micelles
    K. Schäfer, H.B. Kolli, M.K. Christensen, SLB, G. Diezemann, J. Gauss, G. Milano, R. Lund, M. Cascella
    Angew. Chem. 59, 2-10 (2020)

  17. Automated Determination of Hybrid Particle-Field Parameters by Machine Learning
    M. Ledum, SLB, M. Cascella
    Mol. Phys. 118, e1785571 (2020) [arXiv] [Presentation]
  18. Hybrid Particle-Field Molecular Dynamics Under Constant Pressure
    SLB, H.B. Kolli, A. De Nicola, M. Byshkin, T. Kawakatsu, G. Milano, M. Cascella
    J. Chem. Phys. 152, 184908 (2020) [arXiv]
  19. High-Resolution Large Time-Step Schemes for Inviscid Fluid Flow
    SLB, T. Flåtten
    Appl. Math. Model. 81, 263-278 (2020)
  20. Mesoscale electrostatics driving particle dynamics in non-homogeneous dielectrics
    SLB, H.B. Kolli, T. Kawakatsu, G. Milano, M. Cascella
    J. Chem. Theory Comput. 15, 2033 (2019) [ChemRxiv]
  21. Hybrid Particle-Field Molecular Dynamics Simulations of Charged Amphiphiles in Aqueous Environment
    Hima Bindu Kolli, A. De Nicola, SLB, K. Schäfer, G. Diezemann, J. Gauss, T. Kawakatsu, Z. Lu, Y. Zhu, G. Milano, M. Cascella
    J. Chem. Theory Comput. 14, 4928-4937 (2018) [ChemRxiv]
  22. Hybrid Particle-Field Model for Conformational Dynamics of Peptide Chains
    SLB, G. Milano, M. Cascella
    J. Chem. Theory Comput. 14, 1120-1130 (2018)
  23. Coupling spin to velocity: collective motion of Hamiltonian polar particles
    SLB, M. Schindler, K.N. Thu Lam, E. Bertin, O. Dauchot
    J. Stat. Mech. 3, 033305 (2016) [arXiv]


  1. Kutt ut prosjekt­beskrivelser i doktorgrads­søknader (Khrono, 2023)
  2. Snø i Japan [PDF] (Klassekampen, 2021)
  3. Corona-problemet [PDF] (Klassekampen, 2020)
  4. I anledning telefonkioskens dag [PDF] (Aftenbladet, 2020)
  5. Den hellige treenigheten [PDF] [PDF-V2] (Klassekampen, 2019)
  6. Hverdagshykleri [PDF] (Aftenbladet, 2018)
  7. Øyblikket da du blir voksen [PDF] (Klassekampen, 2018)
  8. Hund på bar [PDF] (Klassekampen, 2018)
  9. Ukultur hos havets kardinaler [PDF] (Klassekampen, 2018)
  10. Glem ikke å sette opp ølteltet (Universitas, 2017)
  11. Make Frederikke greit again (Universitas, 2016)

Talks and presentations:

Meeting and conference abstracts:

Review activity:

Examiation assignments:

Other academic work:

PhD thesis

Master thesis

Projects and reports