From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
Authors: Timothée Devergne, Vladimir Kostic, Michele Parrinello, Massimiliano Pontil
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we test the method described above on well-established [14, 9, 32, 36] molecular dynamics benchmarks, featuring biased simulations of increasing complexity. We first start by showing the efficiency of our method on a simple one dimensional double well potential. |
| Researcher Affiliation | Academia | Timothée Devergne CSML & ATSIM, Istituto Italiano di Tecnologia timothee.devergne@iit.it Vladimir R. Kostic CSML, Istituto Italiano di Tecnologia University of Novi Sad vladimir.kostic@iit.it Michele Parrinello ATSIM, Istituto Italiano di Tecnologia michele.parrinello@iit.it Massimiliano Pontil CSML, Istituto Italiano di Tecnologia AI Centre, University College London massimiliano.pontil@iit.it |
| Pseudocode | Yes | Algorithm 1: From biased to unbiased dynamics via infinitesimal generator |
| Open Source Code | Yes | The codes used to train the models can be found in the following repository: https://github.com/Devergne Timothee/Gen Learn |
| Open Datasets | Yes | The data points are represented in the plane of the distance between the nitrogen atom of the residue 3: ASP (ASP3N) and the oxygen atom of the residue 7: Gly (Gly7O) and the distance between ASP3N and the oxygen atom of residue 8: THR (THR8) which allow visualizing the folded and unfolded states. |
| Dataset Splits | Yes | In all the experiments, the datasets were randomly split into a training and a validation dataset. The proportion were set to 80% for training and 20% for validation. |
| Hardware Specification | Yes | All the experiments were performed on a workstation with a AMD Ryzen threadripper pro 3975wx 32-cores processor and an NVIDIA Quadro RTX 4000 GPU. |
| Software Dependencies | Yes | For all the experiments we used pytorch 1.13, and the optimizations of the models were performed using the ADAM optimizer. The version of python used is 3.9.18. All the simulations are run with GROMACS 2022.3 [2] and patched with plumed 2.10 [45] |
| Experiment Setup | Yes | We use a learning rate of 5.10 3, the architecture of the neural network used is a multilayer perceptron with layers of size 2 (inputs), 20, 20 and 1. The parameter η was chosen to be 0.05. |