TBNAI
Optimizing the Optimizer: Language Models Discover Faster Molecular Relaxation
Publication: Hugging Face Daily Papers
Author: Not available in collected metadata
Source published: 04/10/2026, 20:00:00 UTC
Briefing created: 19/10/2026, 14:12:50 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 09:18:24 UTC.
Briefings
A research team developed AutoSella, a family of two molecular optimization agents that significantly reduce force-call counts compared to the baseline Sella optimizer. The agents use autoresearch to rewrite the optimizer itself, constrained by two admission gates that prevent premature stopping and ensure improvements generalize to unseen molecules. At the r2SCAN-3c DFT level, the best AutoSella variant achieved 40.2--77.2% fewer force calls than Sella while maintaining identical energy reduction, even without using DFT gradients.
Key points
- AutoSella is a family of two molecular optimization agents that reduce force-call counts by 40.2--77.2% compared to Sella at the r2SCAN-3c DFT level.
- The agents use autoresearch to rewrite the optimizer itself, with two admission gates preventing premature stopping and ensuring improvements generalize to unseen molecules.
Context
Geometry optimization is a major cost in quantum-chemical workflows, with each step requiring one force evaluation. This research asks whether a language model can improve on existing methods through autoresearch, specifically testing if agents can rewrite the optimizer itself to minimize force-call counts.
Limitations
The study uses preprint data with no peer review; results are based on held-out molecular benchmarks and potentials not used during the search. The evidence is limited to one specific DFT level (r2SCAN-3c) and does not include force evaluation counts for all other optimization methods or agents tested.
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