LLM-driven framework accelerates perovskite additive discovery

Researchers have developed LEAP, a closed-loop framework that uses a domain-specific large language model combined with active learning to discover additives for perovskite solar cells. This LLM is trained to extract knowledge from scientific literature and represent molecules, which then informs a Bayesian optimization process for prioritizing additives. Experimental validation showed improved additive prioritization, leading to higher power conversion efficiencies in perovskite devices. AI

IMPACT Introduces a novel LLM-driven framework for accelerating materials discovery in photovoltaics.

RANK_REASON The cluster contains an academic paper detailing a new framework for scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bayesian optimization
• large language model
• LEAP
• perovskite solar cells

• paper
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →