mlsanityData to 14 Oct 2026

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

arXiv 2410.21341 ↗First posted 28 Oct 2024cs.LG cs.AIcited by 9

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Abstract (arXiv)

While inorganic retrosynthesis planning is essential in the field of chemical science, the application of machine learning in this area has been notably less explored compared to organic retrosynthesis planning. In this paper, we propose Retrieval-Retro for inorganic retrosynthesis planning, which implicitly extracts the precursor information of reference materials that are retrieved from the knowledge base regarding domain expertise in the field. Specifically, instead of directly employing the precursor information of reference materials, we propose implicitly extracting it with various attention layers, which enables the model to learn novel synthesis recipes more effectively. Moreover, during retrieval, we consider the thermodynamic relationship between target material and precursors, which is essential domain expertise in identifying the most probable precursor set among various options. Extensive experiments demonstrate the superiority of Retrieval-Retro in retrosynthesis planning, especially in discovering novel synthesis recipes, which is crucial for materials discovery. The source code for Retrieval-Retro is available at https://github.com/HeewoongNoh/Retrieval-Retro.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 15 of its 47 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

15 of 47 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  3. 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 645
  4. 2019Multi-Label Image Recognition with Graph Convolutional Networkscited by 322
  5. 2020Retrosynthesis Prediction with Conditional Graph Logic Networkcited by 104
  6. 2017Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classificationcited by 85
  7. 2020RetroXpert: Decompose Retrosynthesis Prediction like a Chemistcited by 68
  8. 2020Learning Graph Models for Retrosynthesis Predictioncited by 65
  9. 2019Predicting materials properties without crystal structure: Deep representation learning from stoichiometrycited by 62
  10. 2022Retrieval-based Controllable Molecule Generationcited by 44
  11. 2023Conditional Graph Information Bottleneck for Molecular Relational Learningcited by 24
  12. 2018Inorganic Materials Synthesis Planning with Literature-Trained Neural Networkscited by 20
3 more
  1. 2023Shift-Robust Molecular Relational Learning with Causal Substructurecited by 15
  2. 2023Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literaturecited by 13
  3. 2023Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformercited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 3.

9 papers.

  1. 2025Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planningcited by 7
  2. 2025Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materialscited by 7
  3. 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
  4. 2025Generative Models for Crystalline Materialscited by 4
  5. 2025DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planningcited by 2
  6. 2026MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planningcited by 2
  7. 2025Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasetscited by 1
  8. 2026MAIL: Memory-driven, Adaptive, Incremental, and Literature-grounded Framework for Hypothesis Generation in Chemistrycited by 0
  9. 2026CondMSP: Condition-Informed and Resource-Aware Inorganic Materials Synthesis Planningcited by 0