Twist and shout: a surprising synergy between aryl and N-substituents defines the computed charge transport properties in a series of crystalline diketopyrrolopyrroles

This is the Accepted Manuscript version of an article accepted for publication in CyrstEngComm. Under embargo. Embargo end date: 22 November 2017. Jesus Calvo-Castrp, Sebastian Macza, Connor Thomson, Graeme Morris, Alan R. Kennedy and Callum J. McHugh, ‘Twist and shout: a surprising synergy between aryl and N-substituents defines the computed charge transport properties in a series of crystalline diketopyrrolopyrroles’, CrysEngComm, Vol 18(48): 9382-9390, first published online 22 November 2016, available at doi: 10.1039/C6CE02261HThe influence of systematic variation of aryl and N-substitution on predicted charge transport behaviour in a series of crystalline diketopyrrolopyrroles is evaluated. A correct combination of substituents is revealed to maximise those properties which dictate device performance in organic single crystals based upon this structural motif. For electron transport, furan and N-alkyl substitution emerge as optimal molecular design strategies, whilst phenyl structures bearing N-benzyl substituents are shown to offer the most significant promise as highly sought after crystalline hole transport materials.Peer reviewedFinal Accepted Versio

Feature Ranking Methods Based on Information Entropy with Parzen Windows

A comparison between several feature ranking methods used on artificial and real dataset is presented. Six ranking methods based on entropy and statistical indices are considered. The Parzen window method for estimation of mutual information and other indices gives similar results as discretization based on the separability index, but results strongly dependent on the # smoothing parameter. The quality of the feature subsets with highest ranks is evaluated by using decision tree, Naive Bayes and the nearest neighbour classifiers. Significant differences are found in some cases, but there is no single best index that works best for all data and all classifiers. To be sure that a subset of features giving the highest accuracy has been selected the use of many different indices is recommended