restructured readme, added DOI

Author: M-R-Schaefer

README.md

@@ -2,7 +2,7 @@
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
-`apax` is a high-performance, extendable package for training of and inference with atomistic neural networks.
+`apax`[1,2] is a high-performance, extendable package for training of and inference with atomistic neural networks.
 It implements the Gaussian Moment Neural Network model [2, 3].
 It is based on [JAX](https://jax.readthedocs.io/en/latest/) and uses [JaxMD](https://github.com/jax-md/jax-md) as a molecular dynamics engine.
 
@@ -91,14 +91,22 @@ The second way is to use the ASE calculator provided in `apax.md`.
 
 Under the supervion of Johannes Kästner
 
-## References
-* [1] DOI PLACEHOLDER
-* [2] V. Zaverkin and J. Kästner, [“Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,”](https://doi.org/10.1021/acs.jctc.0c00347) J. Chem. Theory Comput. **16**, 5410–5421 (2020).
-* [3] V. Zaverkin, D. Holzmüller, I. Steinwart,  and J. Kästner, [“Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,”](https://pubs.acs.org/doi/10.1021/acs.jctc.1c00527) J. Chem. Theory Comput. **17**, 6658–6670 (2021).
-
 
 ## Contributing
 
 We are happy to receive your issues and pull requests!
 
 Do not hesitate to contact any of the authors above if you have any further questions.
+
+
+## Acknowledgements
+
+The creation of Apax was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the framework of the priority program SPP 2363, “Utilization and Development of Machine Learning for Molecular Applications - Molecular Machine Learning” Project No. 497249646 and the Ministry of Science, Research and the Arts Baden-Württemberg in the Artificial Intelligence Software Academy (AISA).
+Further funding though the DFG under Germany's Excellence Strategy - EXC 2075 - 390740016 and the Stuttgart Center for Simulation Science (SimTech) was provided.
+
+
+## References
+* [1] 10.5281/zenodo.10040711
+* [2] F. Zills, M. Schäfer, N. Segreto et. al., ["Collaboration on Machine Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly"]() submitted.
+* [3] V. Zaverkin and J. Kästner, [“Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,”](https://doi.org/10.1021/acs.jctc.0c00347) J. Chem. Theory Comput. **16**, 5410–5421 (2020).
+* [4] V. Zaverkin, D. Holzmüller, I. Steinwart,  and J. Kästner, [“Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,”](https://pubs.acs.org/doi/10.1021/acs.jctc.1c00527) J. Chem. Theory Comput. **17**, 6658–6670 (2021).