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README.md

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 # ChemicalMotifIdentifier
 ![PyPI Version](https://img.shields.io/pypi/v/chemicalmotifidentifier.svg) ![PyPI Downloads](https://static.pepy.tech/badge/chemicalmotifidentifier)
 
-This repository contains the codes necessary to perform a chemical-motif characterization of short-range order, as described in our [Quantifying chemical short-range order in metallic alloys](https://arxiv.org/abs/2311.01545) paper and our [Chemical-motif characterization of short-range order using E(3)-equivariant graph neural networks](https://google.com) paper. 
+This repository contains the codes necessary to perform a chemical-motif characterization of short-range order, as described in our [Quantifying chemical short-range order in metallic alloys](https://www.pnas.org/doi/abs/10.1073/pnas.2322962121) paper and our [Chemical-motif characterization of short-range order using E(3)-equivariant graph neural networks](https://www.nature.com/articles/s41524-024-01393-5) paper. 
 
 This framework allows for correlating any per-atom property to their local chemical motif. It also allows for the determination of predictive short-range chemical fluctuations length scale. It is based on E(3)-equivariant graph neural networks. Our framework has 100% accuracy in the identification of *any* motif that could ever be found in an fcc, bcc, or hcp solid solution with up to 5 chemical elements.