Crystal Transformer Based Universal Atomic Embedding for Accurate and Transferable Prediction of Materials Properties
arxiv(2024)
摘要
In this work, we propose a novel approach to generate universal atomic
embeddings, significantly enhancing the representational and accuracy aspects
of atomic embeddings, which ultimately improves the accuracy of property
prediction. Moreover, we demonstrate the excellent transferability of universal
atomic embeddings across different databases and various property tasks. Our
approach centers on developing the CrystalTransformer model. Unlike traditional
methods, this model does not possess a fundamental graph network architecture
but utilizes the Transformer architecture to extract latent atomic features.
This allows the CrystalTransformer to mitigate the inherent topological
information bias of graph neural networks while maximally preserving the atomic
chemical information, making it more accurate in encoding complex atomic
features and thereby offering a deeper understanding of the atoms in materials.
In our research, we highlight the advantages of CrystalTransformer in
generating universal atomic embeddings through comparisons with current
mainstream graph neural network models. Furthermore, we validate the
effectiveness of universal atomic embeddings in enhancing the accuracy of model
predictions for properties and demonstrate their transferability across
different databases and property tasks through various experiments. As another
key aspect of our study, we discover the strong physical interpretability
implied in universal atomic embeddings through clustering and correlation
analysis, indicating the immense potential of our universal atomic embeddings
as atomic fingerprints.
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