Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge

Summary

I'm not an expert in this field. I'm familiar with organic retrosynthesis prediction but not familiar with inorganic retrosynthesis planning. This paper first trains a retriever to determine which materials to reference. Then this paper trains a model for material selections.

Strengths

1. This inorganic retrosynthesis planning is different from organic retrosynthesis planning. The proposed method is novel in my opinion. Inorganic retrosynthesis planning operates step by step while this paper proposes the material within one step. 2. Writing is clear. I can follow this paper. 3. The proposed method achieves good performance.

Weaknesses

1. Little discussion on the difference between inorganic retrosynthesis planning and organic retrosynthesis planning. 2. Little discussion on computational complexity and the space of the material.

Questions

N/A

Rating

6

Confidence

2

Soundness

3

Presentation

3

Contribution

3

Limitations

N/A

Summary

This paper lies in the domain of AI for chemistry, and this paper proposes RetroPLEX for inorganic retrosynthesis planning. The proposed approach is comprised of two components: masked precursor completion retriever and neural reaction energy retriever.

Strengths

The writing is mostly clear. The experiment results are thoroughly discussed.

Weaknesses

The model training subsection has not detailedly elaborated on the training process of the proposed approach. The authors are encouraged to provide the pseudocode of the proposed approach.

Questions

Can the proposed approach provide some insights into the AI methods for other domains, e.g., robotics and mixed integer linear programming?

Rating

5

Confidence

1

Soundness

2

Presentation

3

Contribution

2

Limitations

The authors have discussed the potential limitations in the Appendix.

Summary

The manuscript presents a approach, RetroPLEX, for inorganic retrosynthesis planning. The authors propose RetroPLEX, a method that implicitly extracts precursor information from reference materials using attention layers. Additionally, they incorporate domain expertise by considering the thermodynamic relationships between target materials and potential precursors. The manuscript presents a approach, RetroPLEX, for inorganic retrosynthesis planning. The authors propose RetroPLEX, a method that implicitly extracts precursor information from reference materials using attention layers. Additionally, they incorporate domain expertise by considering the thermodynamic relationships between target materials and potential precursors.

Strengths

1. The idea of implicitly extracting precursor information from reference materials using retrieval and attention mechanisms is interesting and potentially beneficial for discovering new synthesis recipes. 2. The incorporation of thermodynamic relationships (∆G) through the NRE retriever is a useful addition, reflecting domain knowledge in inorganic synthesis. 3. The paper includes a variety of experiments comparing RetroPLEX to existing methods.

Weaknesses

1. The method is pretty complex, including two retrievers, MPC and NRE. The complex structure, while powerful, could pose challenges in terms of computational efficiency and scalability, especially when deployed in resource-limited settings. 2. The manuscript lacks clarity in several areas. For example, the explanation of the NRE retriever's training process is unclear.

Questions

1. Why were the MPC and NRE retrievers chosen? How do they complement each other? 2. Given the apparent complexity of the proposed model, what are the computational requirements? Can the authors provide a detailed analysis of the time and resource requirements for training and using RetroPLEX? 3. How does the model's performance scale with increasing dataset sizes or computation complexity? 4. Can the authors provide qualitative examples of the importance of each retriever? For example, provide examples where each retriever plays a crucial role in identifying relevant reference materials or extracting precursor information.

Rating

5

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors have discussed the limitations.

Summary

The authors address the domain of inorganic retrosynthesis by better leveraging existing inorganic retrosynthesis data. They employ attention learning techniques to establish relationships between chemical formulas and precursor formulas. Additionally, they utilize a neural reaction energy predictor to forecast the Gibbs free energy of chemical reactions, thereby refining the candidate list. The integration of these two models significantly enhances the accuracy of the prediction results. This approach has strong applications in the exploration of material synthesis.

Strengths

1. Gibbs free energy is essential for determining if a chemical reaction can occur spontaneously and is more likely to happen. Considering this factor is necessary and has been overlooked in previous works. The inclusion of the neural reaction energy predictor significantly improves prediction results by providing a more accurate assessment of Gibbs free energy for chemical reactions. 2. In the task of inorganic retrosynthesis, predicting the class of a precursor compound from its chemical formula is crucial. There are few models for this task starting from chemical formulas. The authors provide a detailed comparison with models like Roost and CrabNet and evaluate variables such as the number of precursors used. The experiments are thorough and well-documented.

Weaknesses

1. Chemical formula representation issues, detailed in Question 4. 2. Limited improvement over random selection, detailed in Question 5.

Questions

1. The Gibbs free energy data used by the authors is used to train a prediction model, and the authors fine-tuned the model to align with experimental values. However, Gibbs free energy depends on specific conditions such as temperature and pressure during experiments or calculations. It's unclear what the specific conditions of reactions were during this transfer process. 2. In section 3.1, the authors mentioned "The overall training procedure of MPC retriever is in Figure 1 (a)." It seems this should actually refer to Figure 2. 3. In Table 8, under Qualitative Analysis, the authors predict the precursors for Na3Dy(PO4)2 using MPC to find a similar compound Na3Y(PO3)4. However, this compound cannot be found in online materials databases. Moreover, the claimed corresponding precursor sets do not seem capable of synthesizing Na3Y(PO3)4. Please provide the data source and specific synthesis literature for this compound. 4. The authors use chemical formulas as input to their model. However, in practical synthesis tasks, different conditions can yield different structures with the same chemical formula. The authors should clarify how they addressed the issue of duplicate chemical formulas during the data preprocessing stage. 5. In section 4.3, the authors note that "reference materials are randomly retrieved without using trained retrievers" and still achieve a top-1 accuracy of 58%. This high accuracy is surprising and raises questions about the effectiveness of the trained retrievers. The authors should explain why random selection of reference materials performs nearly as well as the trained retrievers and discuss the underlying reasons for this result.

Rating

6

Confidence

5

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors use Gibbs free energy to evaluate the synthesis of reference materials. However, predicting Gibbs free energy can be challenging, especially when only the chemical formula is available without specific structures, synthesis conditions, or information about gas release during synthesis. This limitation suggests an area for improvement in making energy predictions more accurate. Additionally, the method of synthesizing inorganic materials—whether through heating and calcination, microwave, or other techniques—can also affect the choice of precursor compounds. Incorporating these synthesis methods and their specific impacts on precursor selection would further enhance the model's accuracy and applicability. We hope the authors can consider these factors in future improvements to their approach.

Summary

The paper introduces RetroPLEX, a method for inorganic retrosynthesis planning. It extracts precursor information from retrieved reference materials implicitly. The authors use attention layers to extract information from the reference material and design a neural reaction energy (NRE) retriever to provide complementary reference materials. Extensive experiments demonstrate the effectiveness of implicit extraction of precursor information and NRE retriever in discovering novel synthesis recipes.

Strengths

Originality: RetroPLEX presents a new approach to inorganic retrosynthesis planning by implicitly extracting precursor information from retrieved reference materials. This deviates from traditional methods that rely on explicit utilization of precursor information. Quality: The authors provide a comprehensive evaluation of RetroPLEX, including assessments in realistic scenarios, which demonstrates its effectiveness in discovering novel synthesis recipes. Clarity: The paper is well-written and easy to follow, with clear explanations of the methodology and results. Significance: The proposed method has significant implications for material science, as it can aid in the discovery of new materials and their synthesis routes.

Weaknesses

No specific complaint. The work is highly praised for its comprehensiveness, thoroughness, and originality. It is supported by ample evidence that attests to its quality. The following section will include questions related to the work.

Questions

How does RetroPLEX handle situations where there are multiple possible synthesis routes for a target material? How do the authors plan to address the potential issue of overfitting, given the complexity of the model?

Rating

7

Confidence

5

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors acknowledge limitations of their work, such as the importance of incorporating precursor information from a broader range of reference materials.