Simulation of charge transport in amorphous organic semiconductors

Viele Anwendungen der organischen Elektronik wie beispielsweise organische Leuchtdioden (OLEDs) oder organische Photovoltaik (OPV) basieren auf amorphen halbleitenden Molekülen. Die fast unendlichen Variationsmöglichkeiten organischer Materialien erschweren gezielte experimentelle Materialentwicklung. Bestehende Theorien für den Ladungstransport in amorphen Materialien basieren weitgehend auf empirischen Materialparametern und können so nicht zur prädiktiven Vorhersage von Eigenschaften neuer Materialien benutzt werden. In dieser Arbeit werden Modelle entwickelt, die dem Zweck dienen, den Zusammenhang zwischen mikroskopischen Moleküleigenschaften und deren makroskopischer Leitfähigkeit zu verstehen und somit die Ladungsträgermobilität neuer Materialien vorauszusagen. Zwei Aspekte stellen bei der Entwicklung von Modellen für den Ladungstransport in amorphen Materialien besondere Herausforderungen dar. Zum einen müssen Effekte auf vielen Größenskalen berücksichtigt werden, die von der elektronischen Struktur einzelner Moleküle in Subnanometerbereich bis hin zu Perkolationseffekten im Mikrometermaßstab reichen. Zum anderen hängt die Ladungsträgermobilität exponentiell von der Energieunordnung im amorphen Material ab. Diese wird von der Konformation einzelner Moleküle sowie von deren Wechselwirkung mit ihrer ungeordneten Umgebung bestimmt. Um die Effekte auf allen Größenskalen zu berücksichtigen, wird in dieser Arbeit ein Multiskalenmodell zur Simulation von Ladungstransport in organischen Halbleitern vorgestellt. Die Energieunordnung atomar aufgelöster Morphologien wird mithilfe der Quantum Patch Methode bestimmt, die die elektronische Struktur amorpher Moleküle selbstkonsistent bestimmt. Die mit diesem Modell berechnete Ladungsträgermobilität zeigt gute Übereinstimmung mit experimentellen Daten. Darüber hinaus erlaubt das Modell eine Zerlegung der Ladungsträgermobilität in Faktoren, die von einzelnen Moleküleigenschaften abhängen. Dies ermöglicht die Ableitung von Designkriterien für neue organische Moleküle. Mithilfe dieser Kriterien wurde die Elektronenmobilität eines bekannten Materials durch Änderung der chemischen Struktur gezielt erhöht. Bei dieser Modifikation wurden die Energielevels bewusst konstant gehalten, um optische Eigenschaften nicht zu verändern. Das somit gewonnene Material wurde synthetisiert und elektronisch charakterisiert. In Übereinstimmung mit den theoretischen Vorhersagen zeigt das Material eine um drei Größenordnungen erhöhte Elektronenmobilität. Dieses Beispiel demonstriert die Durchführbarkeit von in-silico Materialentwicklung

Molecular origin of the anisotropic dye orientation in emissive layers of organic light emitting diodes

Molecular orientation anisotropy of the emitter molecules used in organic light emitting diodes (OLEDs) can give rise to an enhanced light-outcoupling efficiency, when their transition dipole moments are oriented preferentially parallel to the substrate, and to a modified internal quantum efficiency, when their static dipole moments give rise to a locally modified internal electric field. Here, the orientation anisotropy of state-of-the-art phosphorescent dye molecules is ivestigated using a simulation approach which mimics the physical vapor deposition process of amorphous thin films. The simulations reveal for all studied systems significant orientation anisotropy. Various types are found, including a preference of the static dipole moments to a certain direction or axis. However, only few systems show an improved outcoupling efficiency. The outcoupling efficiency predicted by the simulations agrees with experimentally reported values. The simulations reveal in some cases a significant effect of the host molecules, and suggest that the driving force of molecular orientation lies in the molecule-specific van der Waals interactions of the dye molecule within the thin film surface. The electrostatic dipole-dipole interaction slightly reduces the anisotropy. These findings can be used for the future design of improved dye molecules

Implementing graph neural networks with TensorFlow-Keras

Graph neural networks are a versatile machine learning architecture that received a lot of attention recently. In this technical report, we present an implementation of convolution and pooling layers for TensorFlow-Keras models, which allows a seamless and flexible integration into standard Keras layers to set up graph models in a functional way. This implies the usage of mini-batches as the first tensor dimension, which can be realized via the new RaggedTensor class of TensorFlow best suited for graphs. We developed the Keras Graph Convolutional Neural Network Python package kgcnn based on TensorFlow-Keras that provides a set of Keras layers for graph networks which focus on a transparent tensor structure passed between layers and an ease-of-use mindset

Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn)

Graph neural networks are a versatile machine learning architecture that received a lot of attention recently due to its wide range of applications. In this technical report, we present an implementation of graph convolution and graph pooling layers for TensorFlow-Keras models, which allows a seamless and flexible integration into standard Keras layers to set up graph models in a functional way. We developed the Keras Graph Convolutional Neural Network Python package kgcnn based on TensorFlow-Keras which focus on a transparent tensor structure passed between layers and an ease-of-use mindset

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms

Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types. We propose an alternative approach of generating synthetic crystals with random coordinates by using the symmetry operations of each space group. Based on this approach, we demonstrate online training of deep ResNet-like models on up to a few million unique on-the-fly generated synthetic diffractograms per hour. For our chosen task of space group classification, we achieved a test accuracy of 79.9% on unseen ICSD structure types from most space groups. This surpasses the 56.1% accuracy of the current state-of-the-art approach of training on ICSD crystals directly. Our results demonstrate that synthetically generated crystals can be used to extract structural information from ICSD powder diffractograms, which makes it possible to apply very large state-of-the-art machine learning models in the area of powder X-ray diffraction. We further show first steps toward applying our methodology to experimental data, where automated XRD data analysis is crucial, especially in high-throughput settings. While we focused on the prediction of the space group, our approach has the potential to be extended to related tasks in the future

3DSC - A New Dataset of Superconductors Including Crystal Structures

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, which are a highly interesting but also a complex class of materials with many relevant applications, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset ('3DSC'), featuring the critical temperature $T_\mathrm{c}$ of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database which contains information on the chemical composition, the 3DSC is augmented by the approximate three-dimensional crystal structure of each material. We perform a statistical analysis and machine learning experiments to show that access to this structural information improves the prediction of the critical temperature $T_\mathrm{c}$ of materials. Furthermore, we see the 3DSC not as a finished dataset, but we provide ideas and directions for further research to improve the 3DSC in multiple ways. We are confident that this database will be useful in applying state-of-the-art machine learning methods to eventually find new superconductors.Comment: 15 pages + 10 pages of supporting information; UPDATE: standardised formatting, removed double dash from title & updated github link

MEGAN: Multi-Explanation Graph Attention Network

Explainable artificial intelligence (XAI) methods are expected to improve trust during human-AI interactions, provide tools for model analysis and extend human understanding of complex problems. Explanation-supervised training allows to improve explanation quality by training self-explaining XAI models on ground truth or human-generated explanations. However, existing explanation methods have limited expressiveness and interoperability due to the fact that only single explanations in form of node and edge importance are generated. To that end we propose the novel multi-explanation graph attention network (MEGAN). Our fully differentiable, attention-based model features multiple explanation channels, which can be chosen independently of the task specifications. We first validate our model on a synthetic graph regression dataset. We show that for the special single explanation case, our model significantly outperforms existing post-hoc and explanation-supervised baseline methods. Furthermore, we demonstrate significant advantages when using two explanations, both in quantitative explanation measures as well as in human interpretability. Finally, we demonstrate our model's capabilities on multiple real-world datasets. We find that our model produces sparse high-fidelity explanations consistent with human intuition about those tasks and at the same time matches state-of-the-art graph neural networks in predictive performance, indicating that explanations and accuracy are not necessarily a trade-off.Comment: 9 pages main text, 29 pages total, 19 figure

Scientific intuition inspired by machine learning-generated hypotheses

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space

Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA. We show that our algorithm outperforms other generative models in optimization tasks. We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles.Comment: 9+3 Pages, 7+4 figures, 2 tables. Comments are welcome! (code is available at: https://github.com/aspuru-guzik-group/GA

High‐Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties