Concurrent Optimization of Organic Donor–Acceptor Pairs through Machine Learning. Issue 40 (3rd September 2019)

• Record Type: Journal Article
• Authors: Padula, Daniele; Troisi, Alessandro
• Is Part Of: Advanced energy materials. Volume 9:Issue 40(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-09-03
• Subjects: computational chemistry -- machine learning -- organic photovoltaics; Energy harvesting -- Materials -- Periodicals; Energy conversion -- Materials -- Periodicals; Energy storage -- Materials -- Periodicals; Photovoltaics -- Periodicals; Fuel cells -- Periodicals; Thermoelectric materials -- Periodicals; 621.31
• DOI: 10.1002/aenm.201902463
• Languages: English
• ISSNs: 1614-6832

Abstract

Abstract: 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. Abstract : A set of machine learning models are used to predict the photovoltaic efficiency of organic donor–acceptor pairs based on the electronic and structural properties of both components. Using a data set of experimental observations on 262 donors and 76 acceptors for training, the models developed enable a full exploration of the space of combinations.