Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper proposes to use auto-diff to compute the gradients of the Non-Equilibrium Green Function (NEGF) to solve the quantum transport problem. Numerical differentiation methods are more expensive and can result in numerical errors that could lead to wrong estimations of the physical properties of the system. Moreover, while using Neural Networks as surrogate models is a possible path to achive faster solutions, but because they are trained from data, there's no guarantee that the derived results will be accurate. The paper moves on to briefly describe the NEFG method and specific techniques they employ to compute its derivatives. Finally, the AD-NEGF method is compared against numerical differentiation, showing the correctness of AD-NEGF on one side, while demonstrating the possible pitfalls of relying on numerical differentiation. In addition to establishing the basic benefits of the proposed method, additional experiments are carried out on other settings within this domain.
strength_and_weaknesses: Strengths:
* The first use of autodiff for solving the quantum transport problem that can alleviate many of the problems of earlier approaches.
* Introducing a new topic for the ML community.

Weaknesses:
* The problem this paper aims to solve is too obscure for the ML audience. It would not be a problem if it were introduced more clearly in order to make it more accessible. As a reader with a bit of background in quantum physics but no familiarity with the quantum transport problem, I cannot say that I have a clear understanding of what exactly the authors are trying to solve.
* The comparison to prior works is also a bit unclear. What exactly are black-box methods in this context? It would be useful to provide specific citations to what you are comparing against. Also, What is considered today to be the best performing method and how it compares? Finally, considering that one of the claimed benefits of your method is efficiency, comparing FLOPs or runtime would be crucial. (Beyond the time plot in Figure 6)
* A more minor note is the overuse of initials. It's already hard to follow a new subject, but having so many unfamiliar intials makes reading the paper even harder. 
clarity,_quality,_novelty_and_reproducibility: Clarity: It might be that the paper would be clear to someone more familiar with the subject matter, but to the general ML audience or even those with limited familiarity with quantum physics that paper is difficult to read.

Quality: The comparison to prior works seems lacking. It's not well communicated what are the main methods this work should be compared against and how well it performs.

Novelty: To the extent of my limited understanding, it appears to be a novel application of auto-diff through a physical simulation to solve an intriguing problem. 

Reproducibility: The code is provided, so it should probably be reproducible, but I didn't try running it.
summary_of_the_review: My main concern is that the paper might not be a good fit for the ICLR audience unless a major revision is provided to make it more accessible. While the proposed method belongs to the intersection of ML and Physics and so it could in principle find an audience in ICLR, more space should be spent on introducing the problem to those new to it.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: ****This is an emergency review for the paper 2820****

The paper is about Automatic Differentiation (AD) based quantum transport simulator, and the paper proposes the AD-NEGF algorithm. Key ingredients of the new algorithm efficient gradient calculation by utilizing automatic differentiation and implicit layer techniques and guarantees of correctness of the forward simulation.  The algorithm is used to calculate differential physical quantities and solving inverse problems that are intractable by traditional optimization methods.
strength_and_weaknesses: Strength:
- (I could not identify the strengths of the paper ---other than the contributions stated--- since it is not easy to parse the paper. See below).


Weaknesses:
- The paper does not read as a great fit for the ICLR conference. While the conference is inclusive to new ideas and techniques, the paper focuses on the quantum transport simulation problem, and studies again the non-equilibrium Green function. The reviewer strongly believes that the paper would make a great fit into a more quantum-related conference. It is not clear to the reviewer how this paper would be of relevance to the audience of ICLR (which is mostly focusing on ML/AI topics). This definitely is a negative point for this paper (which might be unfair for its content).
- To the best of the reviewer's understanding, the introduction uses keywords and language that are not known to the average ICLR reviewer: E.g., a sentence that is hard to parse is "The entire numerical process of the quantum transport simulation is implemented in PyTorch, including the computation of the self-energy, the Green function, the electrostatic potential, the transport properties, as well as an optional Slater-Koster Tight-Binding (SKTB) module to generate the block tri-diagonal Tight-Binding (TB) Hamiltonian (Klymenko et al., 2021)." It is not easy for most of the reviewers in ICLR to follow this language in order to appreciate the contributions of this work. That's why the reviewer strongly believes that this paper should resubmitted to a more appropriately selected venue. See also "We propose and implement AD-NEGF, as far as we know the first end-to-end differentiable quantum transport simulator, including the NEGF method, the Poisson’s equation module for self-consistent electrostatic potential computation, and the SKTB module to generate the tight-binding Hamiltonian from the coordinates and properties of the system atoms".
- While the reviewer believes that the math is correct, he/she could not follow the math steps in equations eq. (1)-(4), what they mean, how they are derived and how these are computationally difficult to compute, etc. While introducing new optimization problems in ML/AI venues is more than welcome, that should also come in the form that is easily understandable by the corresponding audience. In other words, it could be the case that the paper could be written in a way that introduces an abstract problem and its efficient solution, and then connect it with the problems considered from the physics perspective. 
- All experiments and results are better suited to a physics or materials science or systems/hardware electrical engineering venue, rather than ICLR. They are not easily appreciated for the problems considered in the current draft.
clarity,_quality,_novelty_and_reproducibility: Clarity: The paper is well-written in terms of use of English; however, it heavily lacks clarity given the average knowledge of the ICLR conference. It is not clear to the reviewer what is the problem at hand, what are the challenges, how this problem is related to the problems mostly highlighted in the ICLR community (and its history). The authors could have done a better job restructuring the paper so that it feels more ML/AI (mathematical computer science/ECE/statistics/applied math) rather than focusing on the material science perspective. 

Quality: The quality cannot be confidently derived given the lack of expertise by the reviewer.

Novelty: Similar as quality

Reproducibility: The authors provide code, so I consider this reproducible.
summary_of_the_review: Out of scope for ICLR. While this should be used sparely (and I'm not comfortable either using it), I find it hard to place this paper in a session in ICLR. Further, the emergency of this review definitely harms the review quality, but I feel strongly that the same outcome would be even if I had a couple more days to review this paper. The idea of automated differentiation for quantum optimal transport could be interesting in other communities, but does not feel a good fit for ICLR. This justifies this score.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper study the problem of quantum transport simulator. It implement an automatic differentiable quantum transport simulator which can provide gradients for sensitivity analysis and solving inverse problems.
strength_and_weaknesses: Strength
1. A automatic differentiable simulator is useful for scientific study of the quantum transport.

Weakness:
1. There is no theoretical novelty of the proposed simulator. It is expected that the gradient based optimization outperforms the BO and Genetic algorithm. ICLR may not be a suitable venue for the work.
2. More discussions on the scalability and runtime is needed to understand the efficiency of the simulator.
clarity,_quality,_novelty_and_reproducibility: The paper writing is clear and easy to understand.
summary_of_the_review: A differentiable simulator for quantum transport.

Thanks for the response. I agree that the work is under the category of AI4Science and is a good infrastructure. So I would like to increase score to 6.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: In this paper, the authors proposed a novel quantum transport simulator, AD-NEGF, that leverages automatic differentiation with Non-Equilibrium Green Function method. The proposed model uses implicit gradient method to accelerate backward gradient computation and is validated via different application scenarios against numerical differentiation and black-box optimization.
strength_and_weaknesses: Strength

* The proposed method is novel and is validated on three different applications.
* The discussion of related works seems extensive.

Weaknesses

* Only qualitative analysis of the empirical result are presented.
* More ablation study is needed to support some of the claims (e.x. the complexity of numerical differentiation and automatic differentiation w.r.t. input and output dimensions). 
clarity,_quality,_novelty_and_reproducibility: The paper is well structured and have sufficient discussion on preliminary knowledge for readers not familiar with the field.
In terms of reproducibility I have no concerns as the authors indicate that the code will be released.
summary_of_the_review: The paper presents a novel quantum transport simulator which is end-to-end differentiable. The proposed model is validated via experiments on three different applications and the results seem to support the claims. Despite that the direction of this paper does not entirely fit in the topics of ICLR, I think the quality of the paper meet the bar for this venue.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: - Summary:

The paper proposes to use auto-diff to compute the gradients of the Non-Equilibrium Green Function (NEGF) to solve the quantum transport problem. Numerical differentiation methods are more expensive and can result in numerical errors that could lead to wrong estimations of the physical properties of the system. The paper moves on to briefly describe the NEFG method and specific techniques they employ to compute its derivatives. The AD-NEGF method is compared against numerical differentiation, showing the correctness of AD-NEGF on one side, while demonstrating the possible pitfalls of relying on numerical differentiation.

- Strengths
Findings of the paper include:
1. The first use of autodiff for solving the quantum transport problem that can alleviate many of the problems of earlier approaches;

- Weaknesses
1. Not a great fit to the ML community (at least, yet)
2. The comparison to prior works is also a bit unclear.
3. The topic, the introduction to it and the whole notation + motivation are not known to the average ICLR reviewer; This connects with weakness #1, but this bullet point also touches upon the clarity/presentation of the paper.
4. There is no theoretical novelty of the proposed simulator; more discussion on the computational aspect of the approach (complexity).

- What would be missing:
While the authors did a good job trying to explain their perspective on the points above, the proposed changes are more than minor to make a decision without another round of reviews. Overall, the authors could/should consider:
1. A better suited venue for this type of work (maybe a quantum information/algorithms conference?)
2. In case the authors are interested in following the ML path, the overall suggestion is to bring this problem closer to what ML attendees know and understand: applications that are closer to ML (the argument about AI4Science is one, but still one of the reviewers complained that there is not enough comparisons + background to understand the alternative AI4Science methodologies for this problem), notation closer to ML community, etc.

justification_for_why_not_higher_score: Overall, there was a consensus that the paper: i) is not a great fit to ICLR; ii) could be explained better or at least in a way more understandable to the ML community; iii) needs better connections to applications in the ML community.
Decision: Reject