From e7e6907ae045564a90d9a0e65abb86aceae01c4f Mon Sep 17 00:00:00 2001 From: Killian Sheriff Date: Wed, 18 Sep 2024 15:29:05 +0200 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a817016..4196317 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # 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.