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Jerome Delhommelle

Affiliations: 
Chemistry The University of North Dakota, Grand Forks, ND, United States 
Area:
Physical Chemistry
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"Jerome Delhommelle"
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Parents

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Alain H. Fuchs grad student 1997-2000 Université Paris-Sud 11

Children

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Tsvetan D. Aleksandrov grad student 2012 University of North Dakota
Kenneth Ndumbe Ngale grad student 2013 University of North Dakota
Bappa Ghosh post-doc University of North Dakota

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Publications

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Desgranges C, Delhommelle J. (2020) Entropy in Molecular Fluids: Interplay between Interaction Complexity and Criticality. The Journal of Physical Chemistry. B
Desgranges C, Delhommelle J. (2020) Unraveling liquid polymorphism in silicon driven out-of-equilibrium. The Journal of Chemical Physics. 153: 054502
Desgranges C, Delhommelle J. (2019) Can Ordered Precursors Promote the Nucleation of Solid Solutions? Physical Review Letters. 123: 195701
Desgranges C, Delhommelle J. (2019) Stabilization of Nanobubbles under Hydrophobic Confinement The Journal of Physical Chemistry C. 123: 11707-11713
Groven SD, Desgranges C, Delhommelle J. (2019) Prediction of the boiling and critical points of polycyclic aromatic hydrocarbons via Wang-Landau simulations and machine learning Fluid Phase Equilibria. 484: 225-231
Valencia-Jaime I, Desgranges C, Delhommelle J. (2019) Viscosity of a highly compressed methylated alkane via equilibrium and nonequilibrium molecular dynamics simulations Chemical Physics Letters. 719: 103-109
Desgranges C, Delhommelle J. (2019) Determination of mixture properties via a combined Expanded Wang-Landau simulations-Machine Learning approach Chemical Physics Letters. 715: 1-6
Desgranges C, Delhommelle J. (2018) Communication: Existence and control of liquid polymorphism in methanol under shear. The Journal of Chemical Physics. 149: 111101
Desgranges C, Delhommelle J. (2018) Prediction of the phase equilibria for island-type asphaltenes via HMC-WL simulations. The Journal of Chemical Physics. 149: 072307
Desgranges C, Delhommelle J. (2018) A new approach for the prediction of partition functions using machine learning techniques. The Journal of Chemical Physics. 149: 044118
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