DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
Authors: Fang Wu, Stan Z. Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on multiple standard MD simulation datasets including MD17 and C7O2H10 isomers. Numerical results demonstrate that DIFFMD constantly outperforms state-of-the-art DLMD models by a large margin. |
| Researcher Affiliation | Academia | Fang Wu1,2, Stan Z. Li1* 1 AI Research and Innovation Laboratory, School of Engineering, Westlake University 2 Institute of AI Industry Research, Tsinghua University fw2359@columbia.edu, stan.zq.li@westlake.edu.cn |
| Pseudocode | Yes | Algorithm 1: Sampling Algorithm with Predictor-Corrector. |
| Open Source Code | No | The paper mentions dataset availability but does not provide concrete access to source code for the described methodology. |
| Open Datasets | Yes | MD17 (Chmiela et al. 2017) and C7O2H10 (Brockherde et al. 2017) datasets are available at http://quantum-machine.org/datasets/ |
| Dataset Splits | Yes | We use the first 20K frame pairs as the training set and split the next 20K frame pairs equally into validation and test sets. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states 'The full experimental details are elaborated in Appendix.' and does not provide specific experimental setup details such as hyperparameter values or training configurations in the main text. |