A Simplified Machine Learning Workflow for Identifying Potential Singlet Fission Candidates: Benzannulated Biphenylenes as a Case Study

Abstract

Singlet fission is a physical phenomenon exhibited by some families of organic materials potentially able to boost the power conversion efficiency of solar cells beyond the theoretical Shockley-Queisser limit of 33%. In order to experience singlet fission, a molecular system must fulfill the so-called energy matching conditions, which can be evaluated using DFT and TD-DFT calculations. Here, we propose a simple protocol that exploits machine learning workflows to screen large libraries of molecules using a reduced number of quantum chemical calculations. The protocol is based on the AQME and ROBERT platforms and is adapted to users with no experience in data science and very basic computational chemistry knowledge.