Leveraging Language Representation for Material Recommendation, Ranking, and Exploration

التفاصيل البيبلوغرافية
العنوان: Leveraging Language Representation for Material Recommendation, Ranking, and Exploration
المؤلفون: Qu, Jiaxing, Xie, Yuxuan Richard, Ciesielski, Kamil M., Porter, Claire E., Toberer, Eric S., Ertekin, Elif
سنة النشر: 2023
المجموعة: Computer Science
Condensed Matter
مصطلحات موضوعية: Condensed Matter - Materials Science, Computer Science - Machine Learning
الوصف: Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning. However, general representations of crystals to explore the vast material search space remain limited. We introduce a material discovery framework that uses natural language embeddings derived from language models as representations of compositional and structural features. The discovery framework consists of a joint scheme that first recalls relevant candidates, and next ranks the candidates based on multiple target properties. The contextual knowledge encoded in language representations conveys information about material properties and structures, enabling both representational similarity analysis for recall, and multi-task learning to share information across related properties. By applying the framework to thermoelectrics, we demonstrate diversified recommendations of prototype structures and identify under-studied high-performance material spaces. The recommended materials are corroborated by first-principles calculations and experiments, revealing novel materials with potential high performance. Our framework provides a task-agnostic means for effective material recommendation and can be applied to various material systems.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2305.01101
رقم الأكسشن: edsarx.2305.01101
قاعدة البيانات: arXiv