About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Addressing Data Needs for High Temperature Material Processing with Natural Language Processing |
| Author(s) |
Amit K. Verma, Benjamin M Glaser, Robin Kuo, Jason Zhang, Nicholas David, Zhisong Zhang, Emma Strubell, Anthony D Rollett |
| On-Site Speaker (Planned) |
Amit K. Verma |
| Abstract Scope |
Data problems persists across many disciplines of materials science, with a particular extreme dearth for high temperature materials where most material attributes need to be determined experimentally. To address this challenge, we are working on two key ideas: 1) data retrieval; and 2) recognition systems for identifying key concepts and their dependencies, from published literature. The first aim to address the lack of open-access experimental data for various machine learning activities, while the second aim to encode the semantics of the domain for bridging various heterogenous data sources. Natural Language Processing (NLP) provide a host of solutions in this regard, and this talk focuses on how NLP is being used to develop the tools mentioned, with specific examples to support our vision. |