Device Fabrication Knowledge Extraction from Materials Science Literature

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

IAAI Technical Track on Emerging Applications of AI

Devices like solar cells, batteries etc. often comprise of a host of material types including organic, inorganic and hybrid materials. The fabrication procedures for these devices involve screening or designing the right set of materials and then subjecting them to a sequence of operations under very specific conditions. The performance characteristics of a device critically depend on the materials used in its fabrication, the specific operations carried out, their operating conditions and the specific sequence in which they are carried out. The space of potential materials, operations and operating conditions is vast, and selecting the right combination thereof to achieve the desired characteristics is a knowledge intensive activity. A large amount of such device fabrication knowledge is available in the form of publications, patents, company reports and so on. In this paper, we present a system that systematically extracts this knowledge from materials science literature. The extracted knowledge is represented as knowledge graphs conforming to an ontology that can be queried to make informed decisions in device fabrication procedures. The system first identifies the set of relevant paragraphs that contain fabrication knowledge. It then employs state of the art entity and relation extraction models to identify instances of operations, methods, materials, etc. and relations between them. The system then applies an unsupervised algorithm to identify sequences of operations representing fabrication procedures. We applied our system on solar cell fabrication knowledge extraction and achieved good performance. We believe our results provide much needed impetus for further work in this area.

10.1609/aaai.v35i17.17811