# Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

arXiv: https://arxiv.org/abs/1711.06872 · HTML: https://mlsanity.com/paper/1711.06872/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text](https://mlsanity.com/paper/2210.12401/) (2022)
- [Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain](https://mlsanity.com/paper/2304.14745/) (2023)
- [Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies](https://mlsanity.com/paper/2207.04502/) (2022)
- [MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain](https://mlsanity.com/paper/2310.15569/) (2023)
- [Structured Extraction of Process Structure Properties Relationships in Materials Science](https://mlsanity.com/paper/2504.03979/) (2025)
- [Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning](https://mlsanity.com/paper/2109.12758/) (2021)

## Abstract (arXiv)

Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles. This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials. In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds. We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text. We also evaluate a variety of supervised models for extracting scientific entities. Our results provide insight into the nature of the data and directions for further work in this exciting new area of research.