Generating π-Functional Molecules Using STGG+ with Active Learning
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).
67b8c05e109d4be55d85d1f0
Alexia Jolicoeur-Martineau
67b8c05e109d4be55d85d1f1
Yan Zhang
67b8c05e109d4be55d85d1f2
Boris Knyazev
extracted_confirmed
2025-02-21T18:14:23.859Z
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/avatars/37cf99dc016c291c800f60d260173482.svg
Boris Knyazev
user
bknyaz
67b8c05e109d4be55d85d1f3
Aristide Baratin
67b8c05e109d4be55d85d1f4
Cheng-Hao Liu
Generating π-Functional Molecules Using STGG+ with Active Learning
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).
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.
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