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README.md

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 # gflownet
 
-gflownet is library upon [PyTorch](https://pytorch.org/) to easily train and extend [GFlowNets](https://arxiv.org/abs/2106.04399), also known as GFN or generative flow networks. GFlowNets are a machine learning framework for probabilistic and generative modelling, with a wide range of applications, especially in [scientific discovery](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00002h) problems.
+gflownet is library built upon [PyTorch](https://pytorch.org/) to easily train and extend [GFlowNets](https://arxiv.org/abs/2106.04399), also known as GFN or generative flow networks. GFlowNets are a machine learning framework for probabilistic and generative modelling, with a wide range of applications, especially in [scientific discovery](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00002h) problems.
 
 In a nutshell, GFlowNets can be regarded as a generative model designed to sample objects $x \in \mathcal{X}$ proportionally to a reward function $R(x)$. This results in the potential of sampling diverse objects with high rewards. For example, given the reward landscape depicted below, defined over a two-dimensional space, a well-trained GFlowNet will be able to sample from the four high-reward corners with high probability.