21 to 30 of 333 Results
Jun 30, 2023 -
Supplementary Material for 'Entropic barrier of water permeation through single-file channels'
Gzip Archive - 21.3 MB -
MD5: d07962502998949580f947c62f1ef616
Simulation and analysis files of tetrameric AQP1 embedded in E. coli PLE membrane model. All-atom simulation using CHARMM36m force field. Structures after 500ns at 277K, 289K, 296K and 309K are also included. Guides how to run and analyse the simulations are included in the workf... |
Jun 30, 2023 -
Supplementary Material for 'Entropic barrier of water permeation through single-file channels'
Gzip Archive - 15.8 MB -
MD5: f48975c095655931b44dea6581c0ee76
Simulation and analysis files of tetrameric AQPZ embedded in E. coli PLE membrane model. All-atom simulation using CHARMM36m force field. Structures after 500ns at 277K, 289K, 296K and 309K are also included. Guides how to run and analyse the simulations are included in the workf... |
Jun 30, 2023 -
Supplementary Material for 'Entropic barrier of water permeation through single-file channels'
Gzip Archive - 600.2 KB -
MD5: 17188dcad81e662ab1df296a90650f97
GROMACS simulation files for CHARMM36m including all special atomtypes from this project (also for CNTs). |
Jun 30, 2023 -
Supplementary Material for 'Entropic barrier of water permeation through single-file channels'
Gzip Archive - 24.4 MB -
MD5: 2fa8beda83c9b939fbefbcb4bde6e374
Simulation and analysis files of four single layered narrow carbon nanotube porins (16 repetition-long) embedded in a DLPC membrane. All-atom simulation using CHARMM36m force field. Structures after 500ns at 289K, 299K, 309K, and 319K are also included. Guides how to run and anal... |
Jun 12, 2023
We aim to use first-principles calculations at finite temperatures in combination with machine learning (ML) techniques to derive an accurate picture of hydrogen embrittlement in Ni-based superalloys. The ML-based interatomic potential will allow for the determination of the temp... |
May 26, 2023 -
Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 70.6 MB -
MD5: 53917b372f27e55e617380404c104c2b
training data with 10 cross-validation split, in cfg format |
May 26, 2023 -
Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 201.0 KB -
MD5: 99ceed914dda02103fbc6e7c11f110de
testing data in cfg format |
May 26, 2023 -
Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
Plain Text - 517 B -
MD5: 3481f8c54ebde1c13d6e016ef5b9d493
INCAR file for VASP which was used for calculations |
May 26, 2023 -
Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 150.8 MB -
MD5: 9bb786377c7162282fa8cbdd11d275da
training data with 10 cross-validation split, in npz format |
May 26, 2023 -
Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 422.9 KB -
MD5: 61b2216e1359cffb4d09edca08980e52
testing data in NPZ format |