LEAP: A closed-loop framework for perovskite precursor additive discovery

TL;DR

LEAP is a novel closed-loop framework combining large language models and active learning to efficiently discover effective perovskite precursor additives, significantly improving solar cell performance.

Contribution

The paper introduces LEAP, a domain-specific LLM integrated with active learning and Bayesian optimization for additive discovery in perovskite solar cells, demonstrating improved screening and device efficiency.

Findings

LEAP outperforms general-purpose models in mechanistic reasoning.

Experimental validation shows improved additive prioritization.

Achieved higher PCEs in perovskite devices compared to control.

Abstract

Efficient discovery of precursor additives is essential for improving the performance of perovskite solar cells, yet the large chemical space makes conventional trial-and-error screening inefficient. We develop LEAP(LLM-driven Exploration via Active Learning for Perovskites), an expert-in-the-loop closed framework that couples a domain-specialized large language model(LLM) with active learning for iterative additive prioritization. The LLM is trained to extract mechanism-relevant knowledge from the perovskite additive literature and to represent candidate molecules through interpretable descriptors, which are further integrated into a Bayesian optimization workflow for uncertainty-aware prioritization under low-data conditions. Benchmark results on unseen literature show that the domain-specialized model outperforms general-purpose models in mechanism-consistent reasoning. Experimental validation in an expert-in-the-loop proof-of-concept study suggests improved additive prioritization across three screening rounds, leading to average device PCEs of 20.13% and 20.87% for the later-round 6-CDQ- and 2-CNA-treated devices, respectively, compared with 19.25% for the control, with a champion PCE of 21.32%. These results provide preliminary evidence that literature-grounded mechanistic descriptors, when coupled with Bayesian optimization and expert feasibility review, can support mechanism-aware additive prioritization in perovskite photovoltaics.