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..
JAX MD: A Framework for Differentiable Physics
Authors: Samuel Schoenholz, Ekin Dogus Cubuk
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present several examples that highlight the features of JAX MD including: integration of graph neural networks into traditional simulations, metaoptimization through minimization of particle packings, and a multi-agent flocking simulation. We then provide benchmarks against classic simulation software before describing the structure of the library. |
| Researcher Affiliation | Industry | Samuel S. Schoenholz Google Research: Brain Team EMAIL; Ekin D. Cubuk Google Research: Brain Team EMAIL |
| Pseudocode | No | No clearly labeled pseudocode or algorithm blocks were found; only descriptions of functions and code snippets. |
| Open Source Code | Yes | JAX MD is available at www.github.com/google/jax-md. |
| Open Datasets | Yes | We will use open source data from a recent study of silicon in different crystalline phases [75]. |
| Dataset Splits | Yes | We follow a standard procedure in the field and uniformly sample these trajectories to create 50k configurations that we split between a training set, a validation set, and a test set. |
| Hardware Specification | Yes | Table 1 lists specific hardware for benchmarks: CPU, K80 GPU, and TPU. |
| Software Dependencies | No | The paper mentions several software packages and libraries like JAX, Numpy, Haiku, LAMMPS, and HOOMD-Blue, but does not provide specific version numbers for them. |
| Experiment Setup | Yes | For the Behler-Parrinello architecture we train for 800 epochs using momentum with learning rate 5 10 5 and batch size 10. For the GNN we train for 160 epochs using ADAM with a learning rate of 1 10 3 and batch size 128. |