diff --git a/README.md b/README.md
index a64f4f5..b76099f 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,16 @@
-This package implements fingerprints of the electronic density-of-states (DOS) for the evaluation of similarity of materials based on their electronic structures.
+Fingerprinting of materials based on their electronic structure.
 
-The fingerprints correspond to those introduced in Refs. [1,2]. Our fingerprints allow to target specific energy ranges for the evaluation of the similarity of the electronic structure. 
+Python package to compute fingerprints of the electronic density-of-states (DOS) and evaluate the similarity of materials based on their electronic structures.
+
+This package implements the DOS fingerprints and the similarity metrics introduced in Refs. [1,2]. 
+
+Our DOS fingerprints can be tailored to target specific ranges of the energy spectrum. The computed fingerprints allow for the evaluation of the similarity of the electronic structure.
 
 As a similarity measure we use the Tanimoto coefficient [3].
 
 # Usage
 
-Fingerprints are instances of the `DOSFingerprint()` class and can be calculated by providing the energy in [Joule] and the DOS in [states/unit cell/Joule] to the `calculate()` method. Furthermore, the parameters of a non-uniform grid can be chosen. The default grid is specialized on the energy range between -10 and 5 eV and emphasizes the upper valence region.
+Fingerprints are instances of the `DOSFingerprint()` class and can be calculated by providing the energy in [Joule] and the DOS in [states/unit cell/Joule] to the `calculate()` method. Furthermore, the energy axis can be discretized over a non-uniform grid. For this, specific parameters must be provided. By default, the grid is specialized on the energy range between -10 and 5 eV, thereby emphasizing the upper valence region.
 
 ```Python
 from nomad_dos_fingerprints import DOSFingerprint
@@ -22,21 +26,20 @@ tc = tanimoto_similarity(dos_fingerprint_1, dos_fingerprint_2)
 
 # Citation
 
-If you want to use this package, please cite:
+If you use this package in a publication, please cite it in the following way:
 
 Martin Kuban, Santiago Rigamonti, Markus Scheidgen, and Claudia Draxl:
 "Density-of-states similarity descriptor for unsupervised learning from materials data",
-preprint: https://arxiv.org/abs/2201.02187 
+preprint: https://arxiv.org/abs/2201.02187
 
 # References
 
 [1] Martin Kuban, Santiago Rigamonti, Markus Scheidgen, and Claudia Draxl:
 "Density-of-states similarity descriptor for unsupervised learning from materials data",
-preprint: https://arxiv.org/abs/2201.02187 
+preprint: https://arxiv.org/abs/2201.02187
 
 [2] Martin Kuban, Šimon Gabaj, Wahib Aggoune, Cecilia Vona, Santiago Rigamonti, Claudia Draxl:
-"Similarity of materials and data-quality assessment by fingerprinting"
-preprint: https://arxiv.org/abs/2204.04056
-
+"Similarity of materials and data-quality assessment by fingerprinting",
+MRS Bulletin (2022). https://doi.org/10.1557/s43577-022-00339-w
 
-[3] P. Willet _et al._, J. Chem. Inf. Comput . 38 , 983 996 (1998) (doi:10.1021/ci9800211)
+[3] P. Willet _et al._, J. Chem. Inf. Comput . 38 , 983 996 (1998) (doi:10.1021/ci9800211)
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