Papers

• Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning (2020)
  Julien Horwood and Emmanuel Noutahi
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• Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design (2020)
  Xiufeng Yang and Tanuj Kr Aasawat and Kazuki Yoshizoe
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• Symmetry-Aware Actor-Critic for 3D Molecular Design (2020)
  Gregor N. C. Simm et al.
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• AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design (2023)
  Romain Lacombe et al.
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