Quantitative structure‐property relationship modeling of small organic molecules for solar cells applications

Abstract

Despite the need of a reliable technology for solar energy harvesting, research on new materials for third generation photovoltaics is slowed down by the diffuse use of trial and error rather than rational material design approaches. The proposed study investigates the use of alternative strategies to material discovery inspired by drug design and molecular modeling. In particular, training set and test set (for validation purposes) comprising well‐known small molecule‐bulk heterojunction organic photovoltaics were built. Molecules were characterized by semiempirical calculated and 3D molecular interaction fields–based descriptors. Then partial least squares algorithm was applied to rationalize structure‐photovoltaic activity relationships, and coefficients were investigated to clarify contributions played by the different molecular properties to the final performance. In addition, a photovoltaic desirability function (PhotD) was also proposed as alternative and versatile novel tool for ranking potential candidates. The partial least squares model and PhotD function were both internally and externally validated demonstrating their ability in estimating new candidates performances. The proposed approach demonstrates that, in the context of computational materials science, chemometrics and molecular modeling tools could effectively boost the discovery of novel promising candidates for photovoltaic application.

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

“…The predictive power of a theoretical model by high‐throughput virtual screening has been explored in the recent years . For example, in the Harvard Clean Energy Project (CEP), Aspuru‐Guzik and co‐workers have screened ≈2.3 million compounds by employing the Scharber model to find out efficient new donor molecules for OPVs.…”

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include the strong electron-electron interactions and strong electron-phonon couplings as well as the complicated donor/ acceptor (D/A) interface morphology, which are fundamentally different from inorganic semiconductors. [31][32][33][34][35][36][37][38][39][40] For example, in the Harvard Clean Energy Project (CEP), [41] Aspuru-Guzik and co-workers have screened ≈2.3 million compounds by employing the Scharber model [42,43] to find out efficient new donor molecules for OPVs. These methods are useful for few benchmark systems but cannot be used to explore the chemical space, i.e., screen a large number of candidate materials.

The predictive power of a theoretical model by high-throughput virtual screening has been explored in the recent years.

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“…[31][32][33][34][35][36][37][38][39][40] For example, in the Harvard Clean Energy Project (CEP), [41] Aspuru-Guzik and co-workers have screened ≈2.3 million compounds by employing the Scharber model [42,43] to find out efficient new donor molecules for OPVs. [31][32][33][34][35][36][37][38][39][40] For example, in the Harvard Clean Energy Project (CEP), [41] Aspuru-Guzik and co-workers have screened ≈2.3 million compounds by employing the Scharber model [42,43] to find out efficient new donor molecules for OPVs.…”

Molecular excited states through a machine learning lens

“…ML has been intensively used for the discovery of materials to be employed in optoelectronics, which are mainly organic semiconductors, 74,[180][181][182][183][184] components of bulk heterojunctions for organic photovoltaics (BHJ-OPV), [51][52][53][54][60][61][62]75,76 materials for dye-sensitized solar cells (DSSC) [55][56][57][58][59]63,185,186 and perovskites. 95,111,112,187 ML models have also been developed for the design of fluorescent organic molecules (proteins and others) [47][48][49]68,69,188,189 and photosynthetic complexes 177 that can be used in biological, biomedical and biotechnological research.…”

“…Thus, an estimate of synthesizability alongside performance is highly desirable. 60,63,74 Furthermore, even if promising molecules can be prepared synthetically, their performance strongly depends on the photodevice setup they are used in. ML comes in handy also in this case, because it can be used to find the optimal device setup, process that usually requires lots of manual experimentation.…”