Discovering one molecule out of a million: inverse design of molecular hole transporting semiconductors tailored for perovskite solar cells

TL;DR

This paper presents a hybrid computational and experimental workflow using Bayesian optimization to discover new hole transporting molecules for perovskite solar cells, achieving high efficiency with minimal data.

Contribution

It introduces a closed-loop, data-efficient inverse design method combining high throughput synthesis and machine learning for molecular discovery in optoelectronics.

Findings

Achieved up to 26.23% efficiency in solar cells.

Discovered new molecules with minimal experimental data.

Demonstrated rapid, targeted molecular discovery process.

Abstract

The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexity and literally infinite diversity of conjugated molecular structures present both, an unprecedented opportunity for technological breakthroughs as well as an unseen optimization challenge. Current models rely on big data which do not exist for specialized research films. However, a hybrid computational and high throughput experimental screening workflow allowed us to train predictive models with as little as 149 molecules. We demonstrate a unique closed-loop workflow combining high throughput synthesis and Bayesian optimization that discovers new hole transporting materials with tailored properties for solar cell applications. A series of high-performance molecules were identified from minimal suggestions, achieving up to 26.23% (certified 25.88%) power conversion efficiency in perovskite solar cells. Our work paves the way for rapid, informed discovery in vast molecular libraries, revolutionizing material selection for complex devices. We believe that our approach can be generalized to other emerging fields and indeed accelerate the development of optoelectronic semiconductor devices in general.