Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: 

This paper describes a general purpose differentiable molecular dynamics physics package, JAX MD. It shows several instances, where it simplifies the research process and enables new avenues of work. 

The Github link is provided for reproducible research and future development. It should be encouraged.

I am sure whether this paper fit the ICLR or not, or how deep learning community can benefit from it.

The writing does not feel academic enough sometime. For example,  "Please let us know if there are features that you would find interesting. We are always seeking contributions!" Please consider the rephrase it.
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: The paper presents a python package, called JAX MD for simulating molecular dynamics (MD). JAX MD provides automatic derivations and allows to easily incorporate machine learning models in the MD workflow.

The paper is clearly written and seems technically correct. However, given that I am a specialist of neither package implementation nor physics, I can not really asses that all the details are correct/useful.

Furthermore, even if this work will surely be of great use for the physics community, I am not not sure that the contribution of this paper is sufficient for ICLR. 
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: This paper announces a new software package for simulating molecular dynamics which includes close integration with a neural network / machine learning library--the first to do so.  Straightforward access to hardware acceleration (e.g. GPU) is provided for both the simulation and machine learning.

I lean toward accepting this submission.  If it were only about simulation molecular dynamics using hardware accelerators, I would question the appropriateness of the venue, but because it is explicitly intended to support training and usage of learned potential functions, it seems suitable.  Still might be better placed in a physics/chemistry venue, as where most of the references come from and likely where users would, too. The application area is no doubt an important research technique.  The paper is clearly written, with enough specific examples to contrast previous pain points in this line of work against its smoother interface.  

All of these points are fine for a package-release/tutorial paper, but for a conference paper, might hope to see these addressed:
Description of the elements of the design of JAX which are useful here are presented, and appear distinct from other AD libraries like Tensorflow or PyTorch, although the authors stop short of explicitly stating which functionality would be more difficult/impossible to support with the possible alternatives (automatic vectorization of the simulations seems like one?).
Limitations of the library and drawbacks of any design decisions (something must be traded at some point?) are not explicitly mentioned.  
Despite mentioning numerous existing MD libraries, no performance comparison is drawn against any other.  
Could also show some demonstration of running an experiment which has complexity on par with state of the art research?  The bubble raft example is great for illustrative purposes, but it could be better to save some of that for a tutorial and use space to exercise this library on a relevant problem and show performance there.

I took a quick glance at the code on github; it is substantial but not huge, cleanly organized, and is well-documented.  
review_title: Official Blind Review #3

Meta-review: The paper is about a software library that allows for relatively easy simulation of molecular dynamics. The library is based on JAX and draws heavily from its benefits.

To be honest, this is a difficult paper to evaluate for everyone involved in this discussion. The reason for this is that it is an unconventional paper (software) whose target application centered around molecular dynamics. While the package seems to be useful for this purpose (and some ML-related purposes), the paper does not expose which of the benefits come from JAX and which ones the authors added in JAX MD. It looks like that most of the benefits are built-in benefits in JAX. Furthermore, I am missing a detailed analysis of computation speed (the authors do mention this in the discussion below and in a sentence in the paper, but this insufficient). Currently, it seems that the package is relatively slow compared to existing alternatives. 

Here are some recommendations:
1. It would be good if the authors focused more on ML-related problems in the paper, because this would also make sure that the package is not considered a specialized package that overfits to molecular dynamics.
2. Please work out the contribution/delta of JAX MD compared to JAX.
3. Provide a thorough analysis of the computation speed
4. Make a better case, why JAX MD should be the go-to method for practitioners.

Overall, I recommend rejection of this paper. A potential re-submission venue could be JMLR, which has an explicit software track.
Decision: Reject