1 to 10 of 21,054 Results
May 20, 2026PN 6
We investigate computation through the lens of dynamical systems, unifying physical processes and machine learning. By treating both hardware and algorithms as evolving dynamical systems, we leverage natural physical dynamics - such as relaxation to stable states and phase transitions - as a foundation for robust, efficient computation. Our work sh... |
May 11, 2026 - Surrogate models for groundwater flow simulations
Baratto, Thomas, 2026, "Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics", https://doi.org/10.18419/DARUS-5815, DaRUS, V2, UNF:6:UMWqR0dTYJ/r2z0j/MZJgw== [fileUNF]
Inference package for thermal plume prediction (v1.0.0). Contains pre-trained MLP and random network models, the ba-predict CLI, the csv data used to train the models obtained via simulation by Fabian Böttcher, sample input files, and a Dockerfile. CPU-only - no GPU required. Extract with: tar xzf ba-thermal-plume-v1.0.0.tar.gz && cd ba-thermal-plu... |
ZIP Archive - 51.8 KB -
MD5: d03bca7e27887dc3b1e293cfd3711e1d
Inference source code (release branch): Python package with CLI (ba-predict), MLP and random-weight network implementations, dataset configurations, sample inputs, Dockerfile, and installation files. No model weights included. |
May 11, 2026 - PN 3-11
Korn, Viktoria Helena; Pluhackova, Kristyna, 2026, "Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"", https://doi.org/10.18419/DARUS-5682, DaRUS, V2
GROMACS simulation files, input and final structures for CHARMM36m MD simulations including our refined parameters for phosphorylated serine in 3 different protonation states. The directory charmm36-jul22mod.ff contains our refined parameters. |
May 11, 2026 -
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"
ZIP Archive - 1.1 MB -
MD5: e5e758a42a7a04a3af52bd2a3746f599
Ready-to-use CHARMM36m force field with our parameters for phosphorylated aminoacids |
May 11, 2026 -
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"
ZIP Archive - 1.4 MB -
MD5: 10d788673880f41321b528dd43b8b042
Patched CHARMM36m force field files for CHARMM with our parameters |
May 11, 2026 -
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"
ZIP Archive - 19.4 GB -
MD5: bd30ce5c884d7dda4d9bb1b00d847840
MD sims for calculating relaxation times and Excel sheet with all datapoints, including experimental values. |
May 11, 2026 -
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"
ZIP Archive - 53.9 KB -
MD5: b871e4cc529a90ef9fc5fa93bf225b63
Measured osmotic concentration values for MPA and MAM |
May 11, 2026 -
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"
ZIP Archive - 12.0 GB -
MD5: e87671fc468f8473d2071eb6cbfab36a
osmotic pressure simulations of MP2 with sodium and guanidinium. Also simulations of MP2 with original charmm params scaled to -1 and MP2-1MAM with charge shifted from P to C |
May 11, 2026 -
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"
ZIP Archive - 45.5 MB -
MD5: 56a4af1e874bb6a8c90083dc1b152582
Anti-Sigma Factor Antagonist SpoIIAA simulations wild type and phosphorylated |
