Publications | T. Bereau

Rauer, C.; Bereau, T.: Hydration free energies from kernel-based machine learning: Compound-database bias. The Journal of Chemical Physics 153 (1), 014101 (2020)

Barnett, W.; Bilchak, C. R.; Wang, Y.; Benicewicz, B. C.; Murdock, L. A.; Bereau, T.; Kumar, S. K.: Designing exceptional gas-separation polymer membranes using machine learning. Science Advances 6 (20), eaaz4301 (2020)

Centi, A.; Dutta, A.; Parekh, S. H.; Bereau, T.: Inserting Small Molecules across Membrane Mixtures: Insight from the Potential of Mean Force. Biophysical Journal 118 (6), pp. 1321 - 1332 (2020)

Hoffmann, C.; Centi, A.; Menichetti, R.; Bereau, T.: Molecular dynamics trajectories for 630 coarse-grained drug-membrane permeations. Scientific Data 7 (1), 51 (2020)

Koch, A. H. R.; Morsbach, S.; Bereau, T.; Lévêque, G.; Butt, H.-J.; Deserno, M.; Landfester, K.; Fytas, G.: Probing Nanoparticle/Membrane Interactions by Combining Amphiphilic Diblock Copolymer Assembly and Plasmonics. The Journal of Physical Chemistry B 124 (5), pp. 742 - 750 (2020)

Girard, M.; Bereau, T.: Regulating Lipid Composition Rationalizes Acyl Tail Saturation Homeostasis in Ectotherms. Biophysical Journal 119 (5), pp. 892 - 899 (2020)

Liu, C.; Brandenburg, J. G.; Valsson, O.; Kremer, K.; Bereau, T.: Free-energy landscape of polymer-crystal polymorphism. Soft Matter 16 (42), pp. 9683 - 9692 (2020)

Rudzinski, J. F.; Bereau, T.: Coarse-grained conformational surface hopping: Methodology and transferability. The Journal of Chemical Physics 153 (21), 0031249 (2020)

Scherer, C.; Scheid, R.; Andrienko, D.; Bereau, T.: Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids. Journal of Chemical Theory and Computation 16 (5), pp. 3194 - 3204 (2020)

Bause, M.; Wittenstein, T.; Kremer, K.; Bereau, T.: Microscopic reweighting for nonequilibrium steady-state dynamics. Physical Review E 100 (6), 060103 (2019)

Kanekal, K.; Bereau, T.: Resolution limit of data-driven coarse-grained models spanning chemical space. The Journal of Chemical Physics 151 (16), 164106 (2019)

Menichetti, R.; Bereau, T.: Revisiting the Meyer-Overton rule for drug-membrane permeabilities. Molecular Physics (2019)

Girard, M.; Ehlen, A.; Shakya, A.; Bereau, T.; De La Cruz, M. O.: Hoobas: A highly object-oriented builder for molecular dynamics. Computational Materials Science 167, pp. 25 - 33 (2019)

Hoffmann, C.; Menichetti, R.; Kanekal, K.; Bereau, T.: Controlled exploration of chemical space by machine learning of coarse-grained representations. Physical Review E 100 (3), 033302 (2019)

Menichetti, R.; Kanekal, K.; Bereau, T.: Drug-Membrane Permeability across Chemical Space. ACS Central Science 5 (2), pp. 290 - 298 (2019)

Rudzinski, J. F.; Radu, M.; Bereau, T.: Automated detection of many-particle solvation states for accurate characterizations of diffusion kinetics. The Journal of Chemical Physics 150 (2), 024102 (2019)

Schilling, C.; Mack, T.; Lickfett, S.; Sieste, S.; Ruggeri, F. S.; Šneideris, T.; Dutta, A.; Bereau, T.; Naraghi, R.; Sinske, D. et al.; Knowles, T. P. J.; Synatschke, C. V.; Weil, T.; Knöll, B.: Sequence-Optimized Peptide Nanofibers as Growth Stimulators for Regeneration of Peripheral Neurons. Advanced Functional Materials 29 (24), 1809112 (2019)

Wörner, S. J.; Bereau, T.; Kremer, K.; Rudzinski, J. F.: Direct route to reproducing pair distribution functions with coarse-grained models via transformed atomistic cross correlations. The Journal of Chemical Physics 151 (24), 244110 (2019)

Bereau, T.; DiStasio, R. A.; Tkatchenko, A.; von Lilienfeld, O. A.: Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning. The Journal of Chemical Physics 148 (24), 241706 (2018)

Bereau, T.; Rudzinski, J. F.: Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing Dynamics. Physical Review Letters 121 (25), 256002 (2018)