Premium
Concurrent Optimization of Organic Donor–Acceptor Pairs through Machine Learning
Author(s) -
Padula Daniele,
Troisi Alessandro
Publication year - 2019
Publication title -
advanced energy materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.08
H-Index - 220
eISSN - 1614-6840
pISSN - 1614-6832
DOI - 10.1002/aenm.201902463
Subject(s) - organic solar cell , acceptor , materials science , set (abstract data type) , domain (mathematical analysis) , photovoltaic system , computer science , component (thermodynamics) , work (physics) , artificial intelligence , biological system , thermodynamics , mathematics , mathematical analysis , ecology , physics , composite material , biology , programming language , condensed matter physics , polymer
In this work an instance of the general problem occurring when optimizing multicomponent materials is treated: can components be optimized separately or the optimization should occur simultaneously? This problem is investigated from a computational perspective in the domain of donor–acceptor pairs for organic photovoltaics, since most experimental research reports optimization of each component separately. A collection of organic donors and acceptors recently analyzed is used to train nonlinear machine learning models of different families to predict the power conversion efficiency of donor–acceptor pairs, considering computed electronic and structural parameters of both components. The trained models are then used to predict photovoltaic performance for donor–acceptor combinations for which experimental data are not available in the data set. Data structure, and the usefulness of the trained models are critically assessed by predicting some donor–acceptor pairs that recently appeared in the literature, and the best combinations are proposed as worth investigating experimentally.