| Automated determination of hybrid particle-field parameters by machine learning M Ledum, S Løland Bore, M Cascella Molecular Physics 118 (19-20), e1785571, 2020 | 10 | 2020 |
| HylleraasMD: A Domain Decomposition-Based Hybrid Particle-Field Software for Multiscale Simulations of Soft Matter M Ledum, S Sen, X Li, M Carrer, Y Feng, M Cascella, SL Bore Journal of Chemical Theory and Computation 19 (10), 2939-2952, 2023 | 7 | 2023 |
| HylleraasMD: Massively parallel hybrid particle-field molecular dynamics in Python M Ledum, M Carrer, S Sen, X Li, M Cascella, SL Bore Journal of Open Source Software 8 (84), 4149, 2023 | 4 | 2023 |
| Soft matter under pressure: Pushing particle–field molecular dynamics to the isobaric ensemble S Sen, M Ledum, SL Bore, M Cascella Journal of Chemical Information and Modeling 63 (7), 2207-2217, 2023 | 4 | 2023 |
| On the equivalence of the hybrid particle–field and Gaussian core models M Ledum, S Sen, SL Bore, M Cascella The Journal of Chemical Physics 158 (19), 2023 | 2 | 2023 |
| A Computational Environment for Multiscale Modelling M Ledum | 1 | 2017 |
| Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics M Carrer, HM Cezar, SL Bore, M Ledum, M Cascella | | 2024 |
| Hamiltonian hybrid particle–field method for biological soft matter-Efficient simulation and machine learning approaches M Ledum | | 2022 |
| HylleraasMD: Massively parallel hybrid particle-field M Ledum, M Carrer, S Sen, X Li, M Cascella, SL Bore | | |
