Sigbjørn Løland Bore

University of Oslo — Norway

Materials Science · Physical Sciences

15h-index1.2kcitations63works7.42yr avg
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Research Topics

Machine Learning in Materials Science(21), Spectroscopy and Quantum Chemical Studies(18), Quantum, superfluid, helium dynamics(15), Protein Structure and Dynamics(13), Surfactants and Colloidal Systems(12)

Publications63 total

The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials
Journal of Catalysis·2025Open Access
Learning atomic forces from uncertainty-calibrated adversarial attacks
npj Computational Materials·2025Open Access
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Journal of Chemical Theory and Computation·2025· 46 citedOpen Access
Constraints on the location of the liquid–liquid critical point in water
Nature Physics·2025· 27 cited
The fellowship of the Grignard: 21st century computational tools for hundred-year-old chemistry
Chemical Science·2025· 8 citedOpen Access
Morphology of Lithium Halides in Tetrahydrofuran from Molecular Dynamics with Machine Learning Potentials
2024Open Access
Pinpointing the Location of the Elusive Liquid-Liquid Critical Point in Water
2024· 1 citedOpen Access
Morphology of Lithium Halides in Tetrahydrofuran from Molecular Dynamics with Machine Learning Potentials
2024Open Access
Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics
Journal of Chemical Information and Modeling·2024· 11 citedOpen Access
Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics
2024Open Access
Pinpointing the Location of the Elusive Liquid-Liquid Critical Point in Water
2024· 4 citedOpen Access
Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics
2024Open Access
Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics
2024Open Access
Morphology of lithium halides in tetrahydrofuran from molecular dynamics with machine learning potentials
Chemical Science·2024· 6 citedOpen Access
Many-body potential for simulating the self-assembly of polymer-grafted nanoparticles in a polymer matrix
npj Computational Materials·2023· 14 citedOpen Access
DeePMD-kit v2: A software package for deep potential models
The Journal of Chemical Physics·2023· 456 citedOpen Access
Realistic phase diagram of water from “first principles” data-driven quantum simulations
Nature Communications·2023· 88 citedOpen Access
On the equivalence of the hybrid particle–field and Gaussian core models
The Journal of Chemical Physics·2023· 13 citedOpen Access
Realistic phase diagram of water from "first principles" data-driven quantum simulations
2023· 11 citedOpen Access
HylleraasMD: A Domain Decomposition-Based Hybrid Particle-Field Software for Multiscale Simulations of Soft Matter
Journal of Chemical Theory and Computation·2023· 12 citedOpen Access
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Frequent Co-authors

Michele Cascella(26), Francesco Paesani(17), Giuseppe Milano(12), Morten Ledum(10), Yaoguang Zhai(9), Manuel Carrer(8), Hima Bindu Kolli(7), Alessandro Caruso(6), Samiran Sen(6), Ken Schäfer(5), Gregor Diezemann(5), Jürgen Gauß(5), Toshihiro Kawakatsu(5), Roberto Car(4), Zhishang Luo(4), Jinzhe Zeng(3), Duo Zhang(3), Denghui Lu(3), Pinghui Mo(3), Shaochen Shi(3)