Merge pull request #261 from alexhernandezgarcia/readme

Update README: contributors and papers

README.md

@@ -1,6 +1,19 @@
 # GFlowNet
 
-This repository implements GFlowNets, generative flow networks for probabilistic modelling, on PyTorch. A design guideline behind this implementation is the separation of the logic of the GFlowNet agent and the environments on which the agent can be trained on. In other words, this implementation should allow its extension with new environments without major or any changes to to the agent. Another design guideline is flexibility and modularity. The configuration is handled via the use of [Hydra](https://hydra.cc/docs/intro/).
+This repository implements GFlowNets, generative flow networks for probabilistic modelling, on PyTorch. A design guideline behind this implementation is the separation of the logic of the GFlowNet agent and the environments on which the agent can be trained on. In other words, this implementation facilitates the extension with new environments for new applications. The configuration is handled via the use of [Hydra](https://hydra.cc/docs/intro/).
+
+## Contributors
+
+Many wonderful scientists and developers have contributed to this repository: [Alex Hernandez-Garcia](https://github.com/alexhernandezgarcia), [Nikita Saxena](https://github.com/nikita-0209), [Alexandra Volokhova](https://github.com/AlexandraVolokhova), [Michał Koziarski](https://github.com/michalkoziarski), [Divya Sharma](https://github.com/sh-divya), [Pierre Luc Carrier](https://github.com/carriepl) and [Victor Schmidt](https://github.com/vict0rsch). The GFlowNet implementation was initially part of [github.com/InfluenceFunctional/ActiveLearningPipeline](https://github.com/InfluenceFunctional/ActiveLearningPipeline).
+
+## Research
+
+This repository has been used in at least the following research articles:
+
+- Lahlou et al. [A theory of continuous generative flow networks](https://proceedings.mlr.press/v202/lahlou23a/lahlou23a.pdf). ICML, 2023.
+- Hernandez-Garcia, Saxena et al. [Multi-fidelity active learning with GFlowNets](https://arxiv.org/abs/2306.11715). RealML at NeurIPS 2023.
+- Mila AI4Science et al. [Crystal-GFN: sampling crystals with desirable properties and constraints](https://arxiv.org/abs/2310.04925). AI4Mat at NeurIPS 2023 (spotlight).
+- Volokhova, Koziarski et al. [Towards equilibrium molecular conformation generation with GFlowNets](https://arxiv.org/abs/2310.14782). AI4Mat at NeurIPS 2023.
 
 ## Installation