Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Authors: Leon Klein, Andrew Foong, Tor Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noe, Ryota Tomioka
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Timewarp on small peptide systems. To compare with MD, we focus on the slowest transitions between metastable states, as these are the most difficult to traverse. |
| Researcher Affiliation | Collaboration | Leon Klein Freie Universität Berlin leon.klein@fu-berlin.de Andrew Y. K. Foong Microsoft Research AI4Science andrewfoong@microsoft.com Tor Erlend Fjelde University of Cambridge tef30@cam.ac.uk Bruno Mlodozeniec University of Cambridge bkm28@cam.ac.uk Marc Brockschmidt Sebastian Nowozin Frank Noé Microsoft Research AI4Science Freie Universität Berlin Rice University franknoe@microsoft.com Ryota Tomioka Microsoft Research AI4Science ryoto@microsoft.com |
| Pseudocode | Yes | Pseudocode for the MCMC algorithm is given in Algorithm 1 in Appendix C. Pseudocode is given in Algorithm 2 in Appendix D. |
| Open Source Code | Yes | The code is available here: https://github.com/microsoft/timewarp. |
| Open Datasets | No | The datasets are available upon request3. 3Please contact andrewfoong@microsoft.com for dataset access. |
| Dataset Splits | No | For 2AA and 4AA, we train on a randomly selected trainset of short trajectories (50ns = 108 steps), and evaluate on unseen test peptides. |
| Hardware Specification | Yes | The training was performed on 4 NVIDIA A-100 GPUs for the 2AA and 4AA datasets and on a single NVIDIA A-100 GPU for the AD dataset. Inference with the model as well as all MD simulations were conducted on single NVIDIA V-100 GPUs for AD and 2AA, and on single NVIDIA A-100 GPUs for 4AA. |
| Software Dependencies | No | The paper mentions using 'Open MM library' and 'Deep Speed library' but does not specify their version numbers, which are required for a reproducible description of ancillary software. |
| Experiment Setup | Yes | For all MD simulations we use the parameters shown in Table 1. ... We use a weighted sum of the losses with weights detailed in Table 5. We use the Fused Lamb optimizer and the Deep Speed library [34] for all experiments. The batch size as well as the training times are reported in Table 6. All simulations are started with a learning rate of 5 10 4, the learning rate is then consecutively decreased by a factor of 2 upon hitting training loss plateaus. |