| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Hume-Rothery Symposium on First-Principles Materials Design
|
| Presentation Title |
Advances in Natural Language Processing for Building Datasets in Materials |
| Author(s) |
Elsa Olivetti |
| On-Site Speaker (Planned) |
Elsa Olivetti |
| Abstract Scope |
The heuristic nature of materials synthesis limits researchers' ability to extend materials genome initiative-type approaches to methods to make novel materials or extend existing materials to new, controlled morphologies and microstructures. A number of recent informatics-based approaches have shown that machine learning models can learn synthesis conditions given sufficient data. Machine readable databases of inorganic material fabrication and processing are still limited since the underlying information is present only in unstructured databases such as archives of published scientific literature. Recent work has shown how natural language processing can be used to extract synthesis specific information from texts. The techniques used for these extractions include sequence to sequence tagging algorithms employing recurrent neural networks, large language models, and language dependency parsing using linguistic grammar trees. This presentation will reflect on use of the latest methods in transformer based neural network architecture to extract information from the scientific literature. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, |