Merge pull request #157 from NLESC-JCER/Fix-DOI-paper

Fix DOI

paper/paper.bib

@@ -202,7 +202,7 @@ @article{ANN_QMC
 number = {4},
 pages = {2000269},
 keywords = {Monte Carlo simulations, quantum Monte Carlo simulations, variational Monte Carlo simulations},
-doi = {https://doi.org/10.1002/adts.202000269},
+doi = {10.1002/adts.202000269},
 url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/adts.202000269},
 eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202000269},
 abstract = {Abstract Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, feed-forward neural networks are proposed as a general purpose trial wave function for quantum Monte Carlo simulations of continuous many-body systems. Whereas for simple model systems the whole many-body wave function can be represented by a neural network, the antisymmetry condition of non-trivial fermionic systems is incorporated by means of a Slater determinant. To demonstrate the accuracy of the trial wave functions, an exactly solvable model system of two trapped interacting particles, as well as the hydrogen dimer, is studied.},
@@ -216,7 +216,7 @@ @article{HAN2019108929
 pages = {108929},
 year = {2019},
 issn = {0021-9991},
-doi = {https://doi.org/10.1016/j.jcp.2019.108929},
+doi = {10.1016/j.jcp.2019.108929},
 url = {https://www.sciencedirect.com/science/article/pii/S0021999119306345},
 author = {Jiequn Han and Linfeng Zhang and Weinan E},
 keywords = {Schrödinger equation, Variational Monte Carlo, Deep neural networks, Trial wave-function},