title: Generating π-Functional Molecules Using STGG+ with Active Learning
summary: Generating novel molecules with out-of-distribution properties is a major
  challenge in molecular discovery. While supervised learning methods generate
  high-quality molecules similar to those in a dataset, they struggle to
  generalize to out-of-distribution properties. Reinforcement learning can
  explore new chemical spaces but often conducts 'reward-hacking' and generates
  non-synthesizable molecules. In this work, we address this problem by
  integrating a state-of-the-art supervised learning method, STGG+, in an active
  learning loop. Our approach iteratively generates, evaluates, and fine-tunes
  STGG+ to continuously expand its knowledge. We denote this approach STGG+AL. We
  apply STGG+AL to the design of organic pi-functional materials, specifically
  two challenging tasks: 1) generating highly absorptive molecules characterized
  by high oscillator strength and 2) designing absorptive molecules with
  reasonable oscillator strength in the near-infrared (NIR) range. The generated
  molecules are validated and rationalized in-silico with time-dependent density
  functional theory. Our results demonstrate that our method is highly effective
  in generating novel molecules with high oscillator strength, contrary to
  existing methods such as reinforcement learning (RL) methods. We open-source
  our active-learning code along with our Conjugated-xTB dataset containing 2.9
  million pi-conjugated molecules and the function for approximating the
  oscillator strength and absorption wavelength (based on sTDA-xTB).
paper_authors._id: 67b8c05e109d4be55d85d1f0
paper_authors.name: Alexia Jolicoeur-Martineau
paper_authors._id: 67b8c05e109d4be55d85d1f1
paper_authors.name: Yan Zhang
paper_authors._id: 67b8c05e109d4be55d85d1f2
paper_authors.name: Boris Knyazev
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-21T18:14:23.859Z
paper_authors._id: 63e16165f0039731dfdd442a
paper_authors.avatarUrl: /avatars/37cf99dc016c291c800f60d260173482.svg
paper_authors.fullname: Boris Knyazev
paper_authors.type: user
paper_authors.user: bknyaz
paper_authors._id: 67b8c05e109d4be55d85d1f3
paper_authors.name: Aristide Baratin
paper_authors._id: 67b8c05e109d4be55d85d1f4
paper_authors.name: Cheng-Hao Liu
paper_title: Generating π-Functional Molecules Using STGG+ with Active Learning
paper_summary: Generating novel molecules with out-of-distribution properties is a major
  challenge in molecular discovery. While supervised learning methods generate
  high-quality molecules similar to those in a dataset, they struggle to
  generalize to out-of-distribution properties. Reinforcement learning can
  explore new chemical spaces but often conducts 'reward-hacking' and generates
  non-synthesizable molecules. In this work, we address this problem by
  integrating a state-of-the-art supervised learning method, STGG+, in an active
  learning loop. Our approach iteratively generates, evaluates, and fine-tunes
  STGG+ to continuously expand its knowledge. We denote this approach STGG+AL. We
  apply STGG+AL to the design of organic pi-functional materials, specifically
  two challenging tasks: 1) generating highly absorptive molecules characterized
  by high oscillator strength and 2) designing absorptive molecules with
  reasonable oscillator strength in the near-infrared (NIR) range. The generated
  molecules are validated and rationalized in-silico with time-dependent density
  functional theory. Our results demonstrate that our method is highly effective
  in generating novel molecules with high oscillator strength, contrary to
  existing methods such as reinforcement learning (RL) methods. We open-source
  our active-learning code along with our Conjugated-xTB dataset containing 2.9
  million pi-conjugated molecules and the function for approximating the
  oscillator strength and absorption wavelength (based on sTDA-xTB).
paper_ai_summary: STGG+AL, an active learning approach integrating supervised learning, effectively generates novel molecules with out-of-distribution properties, particularly in designing high-oscillator strength and near-infrared absorptive organic π-functional materials.
paper_ai_keywords: supervised learning reinforcement learning state-of-the-art active learning fine-tuning $\pi$-functional materials oscillator strength near-infrared in-silico validation time-dependent density functional theory Conjugated-xTB dataset sTDA-xTB