B1:Inverse problems in coarse-grained particle simulations
Coarse-graining (CG) is an indispensable tool in computational materials science, but the associated upscaling and downscaling processes have to be designed with great care. Each of these interscale transfers comes with important inverse problems to be solved, most of which are ill-posed or ill-conditioned. In this project, we apply rigorous techniques from the mathematical field of inverse and ill-posed problems to provide a mathematically rigorous foundation of existing and/or new upscaling processes. Furthermore, we develop novel CG algorithms in which one can incorporate thermodynamic constraints in a more natural way.
Coarse-grained model of a nanoscale-segregated ionic liquid for simulations of low-temperature structure and dynamics
Journal of Physics: Condensed Matter33 (20),204002 (2021);
doi:10.1088/1361-648x/abe606
An interplay of excluded-volume and polymer–(co)solvent attractive interactions regulates polymer collapse in mixed solvents
The Journal of Chemical Physics154 (13),134903 (2021);
doi:10.1063/5.0046746
Iterative integral equation methods for structural coarse-graining
The Journal of Chemical Physics154 (8),084118 (2021);
doi:10.1063/5.0038633
Application of the 2PT model to understanding entropy change in molecular coarse-graining
Soft Materials18 (2-3),274-289 (2020);
doi:10.1080/1539445x.2020.1737118
A generalized Newton iteration for computing the solution of the inverse Henderson problem
Inverse Problems in Science and Engineering,1-25 (2020);
doi:10.1080/17415977.2019.1710504
Does Preferential Adsorption Drive Cononsolvency?
Macromolecules52 (11),4131-4138 (2019);
doi:10.1021/acs.macromol.9b00575
A note on the uniqueness result for the inverse Henderson problem
Journal of Mathematical Physics60 (9),093303 (2019);
Highlighted on Scilight, see
https://aip.scitation.org/doi/10.1063/1.5134789 doi:10.1063/1.5112137
Relative entropy indicates an ideal concentration for structure-based coarse graining of binary mixtures
Phys. Rev. E99,053308 (2019);
doi:10.1103/PhysRevE.99.053308
Transferability of Local Density-Assisted Implicit Solvation Models for Homogeneous Fluid Mixtures
J. Chem. Theory Comp15,2881-2895 (2019);
doi:10.1021/acs.jctc.8b01170
Cosolute effects on polymer hydration drive hydrophobic collapse
J. Phys. Chem. B122,3587-3595 (2018);
doi:10.1021/acs.jpcb.7b10780
Addressing the temperature transferability of structure based coarse graining models
Phys.Chem.Chem.Phys20,6617-6628 (2018);
doi:10.1039/c7cp08246k
The Hydrophobic Effect and the Role of Cosolvents
The Journal of Physical Chemistry B121 (43),9986-9998 (2017);
doi:10.1021/acs.jpcb.7b06453
Molecular origin of urea driven hydrophobic polymer collapse and unfolding depending on side chain chemistry
Physical Chemistry Chemical Physics19 (28),18156-18161 (2017);
doi:10.1039/c7cp01743j
Fréchet differentiability of molecular distribution functions I. $$L^\infty $$ L ∞ analysis
Letters in Mathematical Physics108 (2),285-306 (2017);
doi:10.1007/s11005-017-1009-0
Well-Posedness of the Iterative Boltzmann Inversion
Journal of Statistical Physics170 (3),536-553 (2017);
doi:10.1007/s10955-017-1944-2
An inverse problem in statistical mechanics
in Oberwolfach Reports,Editor:Gerhard Huisken,ChapterReport No. 08/2017,EMS,Zürich,Series:Oberwolfach Reports, Vol.14 (2017);
doi:10.4171/OWR/2017/8
Comparison of Different TMAO Force Fields and Their Impact on the Folding Equilibrium of a Hydrophobic Polymer
The Journal of Physical Chemistry B120 (34),8757-8767 (2016);
doi:10.1021/acs.jpcb.6b04100
Study of Hydrophobic Clustering in Partially Sulfonated Polystyrene Solutions with a Systematic Coarse-Grained Model
Macromolecules49 (19),7571-7580 (2016);
doi:10.1021/acs.macromol.6b01132
Comparison of iterative inverse coarse-graining methods
The European Physical Journal Special Topics225 (8-9),1323-1345 (2016);
doi:10.1140/epjst/e2016-60120-1
Mechanism of Polymer Collapse in Miscible Good Solvents
The Journal of Physical Chemistry B119 (51),15780-15788 (2015);
doi:10.1021/acs.jpcb.5b10684
Contact
- Prof. Dr.MartinHanke-Bourgeois
- Institut für Mathematik
- Universität Mainz
- Staudingerweg 9
- D-55128Mainz
- Tel:+49 6131 39 22528
- Fax:+49 6131 39 23331
- hankejl@gBWqURAOdmathematik.uni-mainz.de
- http://www.mathematik.uni-mainz.de/Members/hanke
- Prof. Dr.Nicovan der Vegt
- Institut für Physikalische Chemie
- Technische Universität Darmstadt
- Alarich-Weiss-Straße 10
- D-64287Darmstadt
- Tel:+49 6151 16 4356
- Fax:+49 6151 16 2048
- vandervegtDX_FmHBAswx@Ncpc.tu-darmstadt.de
- http://www.cpc.tu-darmstadt.de/