Pretraining Strategies for Structure Agnostic Material Property Prediction.

ABSTRACT: In recent years, machine learning (ML), especially graph neural network (GNN) models, has been successfully used for fast and accurate prediction of material properties. However, most ML models rely on relaxed crystal structures to develop descriptors for accurate predictions. Generating these relaxed crystal structures can be expensive and time-consuming, thus requiring an additional processing step for models that rely on them. To address this challenge, structure-agnostic methods have been developed, which use fixed-length descriptors engineered based on human knowledge about the material. However, the fixed-length descriptors are often hand-engineered and require extensive domain knowledge and generally are not used in the context of learnable models which are known to have a superior

SUBMITTER: Huang H 

PROVIDER: S-EPMC10865364 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature