Semi-supervised machine-learning classification of materials synthesis procedures
[3];
[1];
[1];
[3];
- Univ. of California, Berkeley, CA (United States). Dept. of Materials Science and Engineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division
- Univ. of California, Berkeley, CA (United States). Dept. of Materials Science and Engineering
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division
Digitizing large collections of scientific literature can enable new informatics approaches for scientific analysis and meta-analysis. However, most content in the scientific literature is locked-up in written natural language, which is difficult to parse into databases using explicitly hard-coded classification rules. In this work, we demonstrate a semi-supervised machine-learning method to classify inorganic materials synthesis procedures from written natural language. Without any human input, latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental materials synthesis steps, such as “grinding” and “heating”, “dissolving” and “centrifuging”, etc. Guided by a modest amount of annotation, a random forest classifier can then associate these steps with different categories of materials synthesis, such as solid-state or hydrothermal synthesis. Finally, we show that a Markov chain representation of the order of experimental steps accurately reconstructs a flowchart of possible synthesis procedures. Our machine-learning approach enables a scalable approach to unlock the large amount of inorganic materials synthesis information from the literature and to process it into a standardized, machine-readable database.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1559267
- Journal Information:
- npj Computational Materials, Vol. 5, Issue 1; ISSN 2057-3960
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Text-mined dataset of inorganic materials synthesis recipes
|
journal | October 2019 |
Similar Records
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Provenance infrastructure for multimodal x-ray experiments and reproducible analysis