| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Ceramics and Glasses Simulations and Machine Learning
|
| Presentation Title |
Application of Natural Language Processing to Zeolites and Cementitious Materials |
| Author(s) |
Elsa Olivetti |
| On-Site Speaker (Planned) |
Elsa Olivetti |
| Abstract Scope |
Advances in applying natural language processing (NLP) to scientific text have been successfully applied to well-studied material systems with large amounts of data. However, we need ways to leverage literature data in materials domains without thousands of papers. Applying NLP pipelines to these types of materials science systems can be challenging due to the general schema and the noisiness of automatically extracted data. In this presentation, we demonstrate how to leverage domain knowledge to build upon existing data extraction techniques and improve extraction accuracy using examples in the zeolite and alternative cement fields. This presentation will describe an effort to integrate artificial intelligence with material science to support the development of low environmental impact concrete mixtures. Generative modeling approaches can be used to learn from this and other data to optimize the design of concrete mixtures. |