Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics

TL;DR

This study combines quantum chemical calculations, statistical analysis, and machine learning to understand and predict the absorption strength of non-fullerene acceptors in organic photovoltaics, aiding molecular design.

Contribution

It introduces a validated computational framework and machine learning models to predict absorption strength based on molecular structure, reducing computational costs.

Findings

TDDFT accurately predicts absorption in solution.

Structural features like planarity and conjugation correlate with high absorption.

Random decision forest provides reliable predictions with lower computational effort.

Abstract

Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of such high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of ~500 pi-conjugated molecules. The calculations are first validated by comparison with experimental measurements on liquid and solid state using common fullerene and non-fullerene acceptors. We find that the molar extinction coefficient ({\epsilon}_(d,max)) shows reasonable agreement between calculation in vacuum and experiment for molecules in solution, highlighting the effectiveness of TDDFT for predicting optical properties of organic pi-conjugated molecules. We then perform a statistical analysis based on molecular descriptors to identify which features are important in defining the absorption strength. This allows us to identify structural features that are correlated with high absorption strength in NFAs and could be used to guide molecular design: highly absorbing NFAs should possess a planar, linear, and fully conjugated molecular backbone with highly polarisable heteroatoms. We then exploit a random decision forest to draw predictions for {\epsilon}_(d,max) using a computational framework based on extended tight-binding Hamiltonians, which shows reasonable predicting accuracy with lower computational cost than TDDFT. This work provides a general understanding of the relationship between molecular structure and absorption strength in pi-conjugated organic molecules, including NFAs, while introducing predictive machine-learning models of low computational cost.