2021
DOI: 10.1002/chem.202103712
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Developing Efficient Small Molecule Acceptors with sp2‐Hybridized Nitrogen at Different Positions by Density Functional Theory Calculations, Molecular Dynamics Simulations and Machine Learning

Abstract: Chemical structure of small molecule acceptors determines their performance in organic solar cells. Multiscale simulations are necessary to avoid trial-and-error based design, ultimately to save time and resources. In current study, the effect of sp 2 -hybridized nitrogen substitution at the inner or the outmost position of central core, side chain, and terminal group of small molecule acceptors is investigated using multiscale computational modelling. Quantum chemical analysis is used to study the electronic … Show more

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Cited by 125 publications
(28 citation statements)
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“…Machine learning will be handy and fast method. On comparing with experimental method, computational method is less cost effective without wasting chemicals and less time for material development [ 31 , 32 , 33 ]. The main benefit of utilizing DFT techniques is a significant expansion in computational precision without increase in the computational time.…”
Section: Resultsmentioning
confidence: 99%
“…Machine learning will be handy and fast method. On comparing with experimental method, computational method is less cost effective without wasting chemicals and less time for material development [ 31 , 32 , 33 ]. The main benefit of utilizing DFT techniques is a significant expansion in computational precision without increase in the computational time.…”
Section: Resultsmentioning
confidence: 99%
“…In organic solar cells research community, machine learning is gaining fame. [26][27][28] However, it has not gained much success as it has gained in image recognition and translation. The major reason is the complex working principle of organic solar cells.…”
Section: Introductionmentioning
confidence: 99%
“…14,25 Actually, the molecular design of efficient solar cells are based primarily on either purely “rational arguments” 16–22 or, more recently, on machine learning tools that have the advantage of being simple and relatively fast. 26–29 In all cases, the need for accurate predictions of the optical properties of dye sensitizers is crucial.…”
Section: Introductionmentioning
confidence: 99%