Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
Authors: Fang Wu, Stan Z. Li
AAAI 2023 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |