# Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

arXiv: https://arxiv.org/abs/2302.02303 · HTML: https://mlsanity.com/paper/2302.02303/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 85 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Recent Advances in Autoencoder-Based Representation Learning](https://mlsanity.com/paper/1812.05069/) (2018)
- [Predicting materials properties without crystal structure: Deep representation learning from stoichiometry](https://mlsanity.com/paper/1910.00617/) (2019)
- [Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks](https://mlsanity.com/paper/1901.00032/) (2018)

## Built on by (most cited first, 13 of 13)

- [Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge](https://mlsanity.com/paper/2410.21341/) (2024)
- [Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning](https://mlsanity.com/paper/2502.04289/) (2025)
- [Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials](https://mlsanity.com/paper/2506.12557/) (2025)
- [A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools](https://mlsanity.com/paper/2506.20743/) (2025)
- [Generative Models for Crystalline Materials](https://mlsanity.com/paper/2511.22652/) (2025)
- [DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning](https://mlsanity.com/paper/2509.17094/) (2025)
- [MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning](https://mlsanity.com/paper/2602.07543/) (2026)
- [Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasets](https://mlsanity.com/paper/2512.02947/) (2025)
- [Accelerated materials language processing enabled by GPT](https://mlsanity.com/paper/2308.09354/) (2023)
- [Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures](https://mlsanity.com/paper/2505.09161/) (2025)
- [A collaborative agent with two lightweight synergistic models for autonomous crystal materials research](https://mlsanity.com/paper/2604.11540/) (2026)
- [Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature](https://mlsanity.com/paper/2607.23886/) (2026)
- [CondMSP: Condition-Informed and Resource-Aware Inorganic Materials Synthesis Planning](https://mlsanity.com/paper/2610.15603/) (2026)

## Abstract (arXiv)

Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2,654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.