Leveraging Language Representation for Material Recommendation, Ranking,
and Exploration
- URL: http://arxiv.org/abs/2305.01101v2
- Date: Sat, 20 May 2023 03:35:30 GMT
- Title: Leveraging Language Representation for Material Recommendation, Ranking,
and Exploration
- Authors: Jiaxing Qu, Yuxuan Richard Xie, Kamil M. Ciesielski, Claire E. Porter,
Eric S. Toberer, Elif Ertekin
- Abstract summary: We introduce a material discovery framework that uses natural language embeddings derived from language models as representations of compositional and structural features.
By applying the framework to thermoelectrics, we demonstrate diversified recommendations of prototype structures and identify under-studied high-performance material spaces.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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