Concurrent Optimization of Organic Donor–Acceptor Pairs through Machine Learning

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.

Performance and Matching Band Structure Analysis of Tandem Organic Solar Cells Using Machine Learning Approaches

“…To assist the development of OSCs, Ma and coworkers proposed two machine‐learning models, gradient‐boosting regression tree and artificial neural network, to demonstrate the positive impact of virtual screening on the identification of 126 candidate molecules . Troisi and Padula have developed a range of machine‐learning algorithms, combining the outcome of quantum chemistry calculation and experimental photovoltaic parameters and predicting the efficiency of binary OSCs with any combination of donor and acceptor materials . All in all, these results showed that machine‐learning approaches coupled with physics/chemical insights can capture complex system behaviors and significantly improve the efficiency of both device and/or material design work.…”

Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning

“…It was indicated that the utilization of larger and more varied databases for training accompanied with higher accuracy. Several predictive models were explored by Padula and Troisi, [ 97 ] who exploited the representation of both electronic or structural parameters as feature inputs at the same time. The predicted values obtained by the KRR model showed good correlations to experimental data (r = 0.7).…”

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

“…For instance, explainable ML models have been learnt to construct QSPR models for predicting different device performances of organic solar cells (OSCs), providing useful suggestions concerning the design of new functional organic materials with desired properties, and contributing to the identication of new OSC materials. [202][203][204][205][206] In 2018, we identied 13 important structural and electronic structure descriptors to describe 280 donor molecules by in-depth understanding of the microscopic mechanism of OSCs. 207 Among them, one is the structural descriptor (number of unsaturated atoms, N atom ), while others, such as polarizability, vertical ionization potential, and holeelectron binding energy, are related to the ground-and excitedstate properties obtained by quantum chemical calculations.…”