# Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

arXiv: https://arxiv.org/abs/1901.00032 · HTML: https://mlsanity.com/paper/1901.00032/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 20 of 20)

- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering](https://mlsanity.com/paper/2303.05352/) (2023)
- [Are LLMs Ready for Real-World Materials Discovery?](https://mlsanity.com/paper/2402.05200/) (2024)
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- [Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks](https://mlsanity.com/paper/2106.13327/) (2021)
- [Machine Learning for a Sustainable Energy Future](https://mlsanity.com/paper/2210.10391/) (2022)
- [Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature](https://mlsanity.com/paper/2302.02303/) (2023)
- [MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks](https://mlsanity.com/paper/2109.08098/) (2021)
- [Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery](https://mlsanity.com/paper/2111.01905/) (2021)
- [Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models](https://mlsanity.com/paper/2302.04914/) (2023)
- [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)
- [Discovering emergent connections in quantum physics research via dynamic word embeddings](https://mlsanity.com/paper/2411.06577/) (2024)
- [A Foundation Model for Material Fracture Prediction](https://mlsanity.com/paper/2507.23077/) (2025)
- [Probing Materials Knowledge in LLMs: From Latent Embeddings to Reliable Predictions](https://mlsanity.com/paper/2603.01834/) (2026)
- [Analyzing Research Trends in Inorganic Materials Literature Using NLP](https://mlsanity.com/paper/2106.14157/) (2021)
- [Structured Extraction of Process Structure Properties Relationships in Materials Science](https://mlsanity.com/paper/2504.03979/) (2025)
- [An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature](https://mlsanity.com/paper/2107.14638/) (2021)
- [Data Augmentation Techniques for Process Extraction from Scientific Publications](https://mlsanity.com/paper/2405.14594/) (2024)
- [Sustainable Materials Discovery in the Era of Artificial Intelligence](https://mlsanity.com/paper/2601.21527/) (2026)

## Abstract (arXiv)

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for arbitrary materials. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds.