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 inorganic synthesis procedures. We created a dataset of 3040 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 inorganic synthesis paragraphs into ULSA and used it to construct synthesis flowcharts for synthesis procedures. Analysis of 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. The present work focuses on the synthesis protocols for solid-state, sol–gel, and solution-based inorganic synthesis, but the language could be extended in the future to include other synthesis methods. This work is an important step towards creating a synthesis ontology and a solid foundation for autonomous robotic synthesis.
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Rapid construction of tetrahydropyridine scaffolds via formal imino Diels–Alder reactions of Schiff bases and Nazarov reagents
Described is a one-flask, two-step method for the synthesis of highly functionalized piperidines. The process involves formal [4 + 2] cycloadditions of Schiff bases and Nazarov reagents, followed by facile elaborations of the initial cycloadducts. Notably, these aza-annulations are facilitated by protic solvents and proceed smoothly under ambient conditions, without other additives. The synthetic utility of this annulation protocol is further showcased through a concise, convergent synthesis of (±)-tetrabenazine.
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- Award ID(s):
- 1566402
- NSF-PAR ID:
- 10338235
- Editor(s):
- Mukherjee, S.
- Date Published:
- Journal Name:
- Organic biomolecular chemistry
- Volume:
- 17
- ISSN:
- 1477-0520
- Page Range / eLocation ID:
- 8827-8831
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Materials discovery has become significantly facilitated and accelerated by high-throughput
ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/C9OB01880H
