Update paper.md

paper/paper.md

@@ -27,7 +27,7 @@ in a physically-motivated neural network. The use of `PyTorch` as a backend to p
 
 # Statement of need
 
-`QMCTorch` is a Python package using `PyTorch` [@pytorch] as a backend to perform Quantum Monte-Carlo (QMC) simulations, namely Variational Monte-Carlo,  of molecular systems. Many software such as `QMCPack`[@qmcpack], `QMC=Chem` [@qmcchem], `CHAMP` [@champ] provide high-quality implementation of advanced QMC methodologies in low-level languages (C++/Fortran).  Python implementations of QMC such as `PAUXY` [@pauxy] and `PyQMC` [@pyqmc] have also been proposed to facilitate the use and development of QMC techniques. Large efforts have been made to leverage recent development of deep learning techniques for QMC simulations with for example the creation of neural-network based wave-function ansatz [@paulinet; @ferminet; @choo_fermionic_2020; @HAN2019108929; @ANN_QMC; @detfree_nn; @fixed_node; @Lin_2023; @ANN_WF] that have lead to very interesting results. `QMCTorch` allows to perform QMC simulations using physically motivated neural network architectures that closely follow the wave function ansatz used by QMC practitioners. Its architecture allows to rapidly explore new functional forms of some key elements of the wave function ansatz. Users do not need to derive analytical expressions for the gradients of the total energy w.r.t. the variational parameters, that are simply obtained via automatic diffentiation. This includes for example the parameters of the atomic orbitals that can be variationally optimized and the atomic coordinates that allows `QMCTorch` to perform geometry optimization of molecular structures. In addition, the GPU capabilities offered by `PyTorch` combined with the parallelization over multiple computing nodes obtained via `Horovod` [@horovod], allow to deploy the simulations on large heterogenous computing architectures. In summary, `QMCTorch` provides QMC practitionners a framework to rapidly protoytpe new ideas and to test them using modern computing ressources.
+`QMCTorch` is a Python package using `PyTorch` [@pytorch] as a backend to perform Quantum Monte-Carlo (QMC) simulations, namely Variational Monte-Carlo,  of molecular systems. Many software such as `QMCPack`[@qmcpack], `QMC=Chem` [@qmcchem], `CHAMP` [@champ] provide high-quality implementation of advanced QMC methodologies in low-level languages (C++/Fortran).  Python implementations of QMC such as `PAUXY` [@pauxy] and `PyQMC` [@pyqmc] have also been proposed to facilitate the use and development of QMC techniques. Large efforts have been made to leverage recent development of deep learning techniques for QMC simulations with for example the creation of neural-network based wave-function ansatz [@paulinet; @ferminet; @choo_fermionic_2020; @HAN2019108929; @ANN_QMC; @detfree_nn; @fixed_node; @Lin_2023; @ANN_WF] that have lead to very interesting results. `QMCTorch` allows to perform QMC simulations using physically motivated neural network architectures that closely follow the wave function ansatz used by QMC practitioners. Its architecture allows to rapidly explore new functional forms of some key elements of the wave function ansatz. Users do not need to derive analytical expressions for the gradients of the total energy w.r.t. the variational parameters, that are simply obtained via automatic differentiation. This includes for example the parameters of the atomic orbitals that can be variationally optimized and the atomic coordinates that allows `QMCTorch` to perform geometry optimization of molecular structures. In addition, the GPU capabilities offered by `PyTorch` combined with the parallelization over multiple computing nodes obtained via `Horovod` [@horovod], allow to deploy the simulations on large heterogeneous computing architectures. In summary, `QMCTorch` provides QMC practitioners a framework to rapidly prototype new ideas and to test them using modern computing resources.
 
 
 # Wave Function Ansatz