ULSA: Unified Language of Synthesis Actions for Representation of Synthesis Protocols

TL;DR

This paper introduces ULSA, a comprehensive language for describing ceramics synthesis procedures, along with a dataset and neural network model, facilitating better extraction and understanding of synthesis protocols for AI-driven material discovery.

Contribution

The paper presents the first unified language for synthesis actions, a labeled dataset of synthesis procedures, and a neural network model to map text into this language, advancing synthesis protocol analysis.

Findings

ULSA covers essential synthesis vocabulary used by researchers.

The neural network effectively maps synthesis paragraphs to ULSA.

Flowchart analysis shows ULSA captures key synthesis features.

Abstract

Applying AI power to predict syntheses of novel materials requires high-quality, large-scale datasets. Extraction of synthesis information from scientific publications is still challenging, especially for extracting synthesis actions, because of the lack of a comprehensive labeled dataset using a solid, robust, and well-established ontology for describing synthesis procedures. In this work, we propose the first Unified Language of Synthesis Actions (ULSA) for describing ceramics synthesis procedures. We created a dataset of 3,040 synthesis procedures annotated by domain experts according to the proposed ULSA scheme. To demonstrate the capabilities of ULSA, we built a neural network-based model to map arbitrary ceramics synthesis paragraphs into ULSA and used it to construct synthesis flowcharts for synthesis procedures. Analysis for the flowcharts showed that (a) ULSA covers essential vocabulary used by researchers when describing synthesis procedures and (b) it can capture important features of synthesis protocols. This work is an important step towards creating a synthesis ontology and a solid foundation for autonomous robotic synthesis.