In this paper we present a method that allows leveraging 3D electron density information to train a deep neural network pipeline to segment regions of high, medium and low electronegativity and classify substances as health hazardous or non-hazardous. We show that this can be used for use-cases such as cosmetics and food products. For this purpose, we first generate 3D electron density cubes using semiempirical molecular calculations for a custom European Chemicals Agency (ECHA) subset consisting of substances labelled as hazardous and non-hazardous for cosmetic usage. Together with their 3-class electronegativity maps we train a modified 3D-UNet with electron density cubes to segment reactive sites in molecules and classify substances with an accuracy of 78.1%. We perform the same process on a custom food dataset (CompFood) consisting of hazardous and non-hazardous substances compiled from European Food Safety Authority (EFSA) OpenFoodTox, Food and Drug Administration (FDA) Generally Recognized as Safe (GRAS) and FooDB datasets to achieve a classification accuracy of 64.1%. Our results show that 3D electron densities and particularly masked electron densities, calculated by taking a product of original electron densities and regions of high and low electronegativity can be used to classify molecules for different use-cases and thus serve not only to guide safe-by-design product development but also aid in regulatory decisions. SCIENTIFIC CONTRIBUTION: We aim to contribute to the diverse 3D molecular representations used for training machine learning algorithms by showing that a deep learning network can be trained on 3D electron density representation of molecules. This approach has previously not been used to train machine learning models and it allows utilization of the true spatial domain of the molecule for prediction of properties such as their suitability for usage in cosmetics and food products and in future, to other molecular properties. The data and code used for training is accessible at https://github.com/s-singh-ivv/eDen-Substances .

We have developed a multi-agent quantum Monte Carlo model to describe the spatial dynamics of multiple majority charge carriers during conduction of electric current in the channel of organic field-effect transistors. The charge carriers are treated by a neglect of diatomic differential overlap Hamiltonian using a lattice of hydrogen-like basis functions. The local ionization energy and local electron affinity defined previously map the bulk structure of the transistor channel to external potentials for the simulations of electron- and hole-conduction, respectively. The model is designed without a specific charge-transport mechanism like hopping- or band-transport in mind and does not arbitrarily localize charge. An electrode model allows dynamic injection and depletion of charge carriers according to source-drain voltage. The field-effect is modeled by using the source-gate voltage in a Metropolis-like acceptance criterion. Although the current cannot be calculated because the simulations have no time axis, using the number of Monte Carlo moves as pseudo-time gives results that resemble experimental I/V curves.

We report a computational study on the chemical bonding of phosphonates and carboxylates to aluminum oxide surfaces and how the binding properties are related to the amount of water in the experimental environment. Two different surface structures were used in the calculations in order to model representative adsorption sites for the phosphonates and carboxylates and to account for the amorphous nature of the hydroxylated AlOx films in experiment For the phosphonates, we find that the thermodynamically preferred binding mode changes between mono-, bi-, and tridentate depending on the surface structure and the amount of residual water. For the carboxylates, on the other hand, monodentate adsorption is always lower in energy at all experimental conditions. Phosphonates are more strongly bound to aluminum oxide than carboxylates, so that carboxylates can be replaced easily by phosphonates. The theoretical findings are consistent with those obtained in adsorption, desorption, and exchange reactions of n-alkyl phosphonic and carboxylic acids on AlOx surfaces. The results provide an atomistic understanding of the adsorption and help to optimize experimental conditions for self-assembly of organic films on aluminum oxide surfaces.

Rigid and soluble electron donor-acceptor conjugates (3 and 11) that combine pi-extended tetrathiafulvalene (exTTF) as an electron donor and C-60 as an electron acceptor have been synthesized using oligo-vinylfluorene (oFV) linkers to connect the electroactive entities. Fluorescence and transient absorption measurements confirm that light-induced excitation induces charge-transfer reactions over distances of 28 angstrom in THF. The incorporation of vinylene spacers into oligofluorene molecular wires has been shown both experimentally and theoretically to improve the charge-transfer features significantly to give a beta value of 0.075 +/- 0.001 angstrom(-1). Enhanced pi-conjugation caused by higher orbital overlap is responsible for this trend.