Active learning enables generation of molecules that advance the known Pareto front

Journal Article · Tue Feb 10 00:00:00 UTC 2026 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-025-01924-8 · OSTI ID:3024470

Antoniuk, Evan R. ^[1]; Li, Peggy ^[1]; Keilbart, Nathan ^[1]; Weitzner, Stephen ^[1]; Kailkhura, Bhavya ^[1]; Hiszpanski, Anna M. ^[1]

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1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 3024470
• Report Number(s): LLNL-JRNL-2001596
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 12; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English