Stuttgart Center for Simulation Science (SC SimTech)

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1 to 10 of 20,957 Results

Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics 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...
ba-thermal-plume-v1.0.0-code.zip May 11, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics 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.
Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching" 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.
charmm36-jul2022mod.ff.zip 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
charmm_toppar_mod.zip 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
nmr_MD_sims.zip 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 DATA MD sims for calculating relaxation times and Excel sheet with all datapoints, including experimental values.
osmotic_experimental.zip 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
osmotic_mp2_ions_MD_sims.zip 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 DATA 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
protein_simulations.zip 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
Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification May 8, 2026 - Usability and Sustainability of Simulation Software Sajith, Durga Lakshmi, 2026, "Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification", https://doi.org/10.18419/DARUS-5914, DaRUS, V1 This dataset contains a training dataset consisting of natural language description-JSON Schema pairs, which can be used to fine-tune LLMs and an evaluation dataset, which can be used to assess the JSON Schema generation capability of LLMs. The evaluation dataset consists of five categories with prompts and their corresponding ground truth schemas....

Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

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...

ba-thermal-plume-v1.0.0-code.zip

May 11, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

ZIP Archive - 51.8 KB -

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.

Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

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.

charmm36-jul2022mod.ff.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 1.1 MB -

Ready-to-use CHARMM36m force field with our parameters for phosphorylated aminoacids

charmm_toppar_mod.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 1.4 MB -

Patched CHARMM36m force field files for CHARMM with our parameters

nmr_MD_sims.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 19.4 GB -

DATA

MD sims for calculating relaxation times and Excel sheet with all datapoints, including experimental values.

osmotic_experimental.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 53.9 KB -

Measured osmotic concentration values for MPA and MAM

osmotic_mp2_ions_MD_sims.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 12.0 GB -

DATA

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

protein_simulations.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 45.5 MB -

Anti-Sigma Factor Antagonist SpoIIAA simulations wild type and phosphorylated

Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification

May 8, 2026 - Usability and Sustainability of Simulation Software

Sajith, Durga Lakshmi, 2026, "Replication Data for: Fine-Tuning Large Language Models for JSON Schema Generation and Modification", https://doi.org/10.18419/DARUS-5914, DaRUS, V1

This dataset contains a training dataset consisting of natural language description-JSON Schema pairs, which can be used to fine-tune LLMs and an evaluation dataset, which can be used to assess the JSON Schema generation capability of LLMs. The evaluation dataset consists of five categories with prompts and their corresponding ground truth schemas....

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