摘要
Predicting the properties of non-fullerene acceptors(NFAs),complex organic molecules used in organic solar cells(OSCs),poses a significant challenge.Some existing approaches primarily focus on atom-level information and may overlook high-level molecular features,including the subunits of NFAs.While other methods that effectively represent subunit information show improved prediction performance,they require labor-intensive data labeling.In this paper,we introduce an efficient molecular description method that automatically extracts molecular information at both the atom and subunit levels without any labor-intensive data labeling.Inspired by the Word2Vec method,our Ring2Vec method treats the“rings”in organic molecules as analogous to“words”in sentences.We achieve fast and accurate predictions of the energy levels of NFA molecules,with a minimal prediction error of merely 0.06 eV.Furthermore,our method can potentially have broad applicability across various domains of molecular description and property prediction,owing to the efficiency of the Ring2Vec model.
基金
We acknowledge the support from PolyU(UGC)project ID P0045695,Hong Kong ITC ITF-ITSP project(project ID P0043294,ITS/028/22FP)
ITC PRP project(ID:PRP/009/22FX)
PolyU-MinshangCT Generative AI Laboratory(Fund No:P0046453)
Research Matching Grant Scheme(Fund No:P0048191)
Research Matching Grant Scheme(Fund No:P0048183)
PolyU Start-up Fund by(Fund No:P0046703).
