Language Models Rewrite Molecular Optimizer for Faster Calculations

Researchers have developed AutoSella, a novel molecular geometry optimizer that utilizes language models to rewrite existing optimization algorithms. This agent-based approach aims to reduce the computational cost of force evaluations, which are a significant bottleneck in quantum-chemical workflows. AutoSella has demonstrated consistent reductions in force calls compared to the previous fastest open-source optimizer, Sella, across various molecular benchmarks, even when applied to density-functional theory calculations without direct DFT gradients. AI

IMPACT Potential to significantly speed up molecular simulations and drug discovery by reducing computational costs.

RANK_REASON Academic paper detailing a new method for optimizing computational chemistry workflows using language models. [lever_c_demoted from research: ic=1 ai=1.0]

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• AutoSella
• density functional theory
• Language Models
• r2SCAN-3c
• Sella

• paper
• infra

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