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The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken , a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization.

Adsorption and desorption of molecules are key processes in extraction and purification of biomolecules, engineering of drug carriers, and designing of surface-specific coatings. To understand the adsorption process on the atomic scale, state-of-the-art quantum mechanical and classical simulation methodologies are widely used. However, studying adsorption using a full quantum mechanical treatment is limited to picoseconds simulation timescales, while classical molecular dynamics simulations are limited by the accuracy of the existing force fields. To overcome these challenges, we propose a systematic way to generate flexible, application-specific highly accurate force fields by training artificial neural networks. As a proof of concept, we study the adsorption of the amino acid alanine on graphene and gold (111) surfaces and demonstrate the force field generation methodology in detail. We find that a molecule-specific force field with Lennard-Jones type two-body terms incorporating the 3rd and 7th power of the inverse distances between the atoms of the adsorbent and the surfaces yields optimal results, which is surprisingly different from typical Lennard-Jones potentials used in traditional force fields. Furthermore, we present an efficient and easy-to-train machine learning model that incorporates system-specific three-body (or higher order) interactions that are required, for example, for gold surfaces. Our final machine learning-based force field yields a mean absolute error of less than 4.2 kJ/mol at a speed-up of ∼10 5 times compared to quantum mechanical calculation, which will have a significant impact on the study of adsorption in different research areas.

Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider poly(3,4-ethylenedioxythiophene) (PEDOT) polystyrene sulfonate, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions and that the electronic couplings can be predicted from the simulated spectra with an 88% accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multiscale computational workflow, they were able to leverage experimental data.

Many molecules used to fabricate organic semiconductor devices carry an intrinsic dipole moment. Anisotropic orientation of such molecules in amorphous organic thin films during the deposition process can lead to the spontaneous buildup of an electrostatic potential perpendicular to the film. This so-called giant surface potential (GSP) effect can be exploited in organic electronics applications and was extensively studied in experiment. However, presently, an understanding of the molecular mechanism driving the orientation is lacking. Here, we model the physical vapor deposition process of seven small organic molecules employed in organic light-emitting diode applications with atomistic simulations. We are able to reproduce experimental results for a wide range of strength of the GSP effect. We find that the electrostatic interaction between the dipole moments of the molecules limits the GSP strength and identify short-range van der Waals interactions between the molecule and the surface during deposition as the driving force behind the anisotropic orientation. We furthermore show how the GSP effect influences the energy levels responsible for charge transport, which is important for the design of organic semiconductors and devices.

In the past, nanoporous metal-organic frameworks (MOFs) have been mostly studied for their huge potential with regard to gas storage and separation. More recently, the discovery that the electrical conductivity of a widely studied, highly insulating MOF, HKUST-1, improves dramatically when loaded with guest molecules has triggered a huge interest in the charge carrier transport properties of MOFs. The observed high conductivity, however, is difficult to reconcile with conventional transport mechanisms: neither simple hopping nor band transport models are consistent with the available experimental data. Here, we combine theoretical results and new experimental data to demonstrate that the observed conductivity can be explained by an extended hopping transport model including virtual hops through localized MOF states or molecular superexchange. Predictions of this model agree well with precise conductivity measurements, where experimental artifacts and the influence of defects are largely avoided by using well-defined samples and the Hg-drop junction approach.

It is an outstanding challenge to model the electronic properties of organic amorphous materials utilized in organic electronics. Computation of the charge carrier mobility is a challenging problem as it requires integration of morphological and electronic degrees of freedom in a coherent methodology and depends strongly on the distribution of polaron energies in the system. Here we represent a QM/QM model to compute the polaron energies combining density functional methods for molecules in the vicinity of the polaron with computationally efficient density functional based tight binding methods in the rest of the environment. For seven widely used amorphous organic semiconductor materials, we show that the calculations are accelerated up to 1 order of magnitude without any loss in accuracy. Considering that the quantum chemical step is the efficiency bottleneck of a workflow to model the carrier mobility, these results are an important step toward accurate and efficient disordered organic semiconductors simulations, a prerequisite for accelerated materials screening and consequent component optimization in the organic electronics industry.

Disordered organic materials have a wide range of interesting applications, such as organic light emitting diodes, organic photovoltaics, and thin film electronics. To model electronic transport through such materials it is essential to describe the energy distribution of the available electronic states of the carriers in the material. Here, we present a self-consistent, linear-scaling first-principles approach to model environmental effects on the electronic properties of disordered molecular systems. We apply our parameter free approach to calculate the energy disorder distribution of localized charge states in a full polaron model for two widely used benchmark-systems (tris(8-hydroxyquinolinato)aluminum (Alq3) and N,N'-bis(1-naphthyl)-N,N'-diphenyl-1,1'-biphenyl-4,4'-diamine (α-NPD)) and accurately reproduce the experimental charge carrier mobility over a range of 4 orders of magnitude. The method can be generalized to determine electronic and optical properties of more complex systems, e.g. guest-host morphologies, organic-organic interfaces, and thus offers the potential to significantly contribute to de novo materials design.