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Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available. Here we present DECIMER — Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.
Due to high power density and superior efficiency, polymer electrolyte membrane fuel cells (PEMFC) are believed to play a significant role for carbon dioxide emissions free electrical energy systems in the future. Unlike in Carnot processes, chemical energy in the form of hydrogen and oxygen is converted directly into electrical energy without a further process step. One issue in the development of PEMFCs for mobile or stationary applications is the utilization of rare and expensive catalyst material like platinum within the membrane electrode assembly (MEA) see figure 1. In addition, the objective is to reduce production costs and to increase the lifetime of PEMFC. One approach to improve PEMFCs is the development of intelligent electrode architectures. However, cost effective high performance materials are necessary to reach the development targets.
Leadership Beyond Narcissism: On the Role of Compassionate Love as Antecedent of Servant Leadership
(2020)
While we already know a lot about the outcomes and boundary conditions of servant leadership, there is still a need for research on its antecedents. Building on the theory of purposeful work behavior and further theorizing by van Dierendonck and Patterson (2015), we examine if leaders’ propensity for compassionate love will evoke servant leadership behavior. At the same time, we contrast compassionate love to leaders’ narcissism as psychological counterpart to compassionate love, because narcissism is not associated with leader effectiveness, but with leader emergence instead. We collected data from 170 leader-follower-dyads in a field study in Germany, while measuring leaders’ compassionate love and narcissism, and followers’ perceptions of servant leadership. We found a positive association between leaders’ compassionate love and servant leadership behavior, while narcissism was negatively associated with servant leadership. Theoretical and practical implications, as well as pathways for future research are discussed.
The purpose of the paper is to contribute to the inner workings of transformational leadership in the context of organizational change. According to the organizational role theory, role conflict is proposed as a mediator between transformational leadership and affective commitment to change and irritation. Cross-sectional data were collected in a German company in the textiles sector, undergoing a pervasive IT-related change. Confirmatory factor analysis and structural equation modeling was performed for validity and hypothesis testing. The findings suggest that role conflict acts as a full mediator in the relationship between transformational leadership and affective commitment to change, as well as irritation. Transformational leadership is often discussed in terms of change-oriented leadership. Surprisingly, only a few studies have examined the specific impact of transformational leadership on attitudinal outcomes during change processes, yet. Consequently, research on the underlying psychological mechanisms of the relationship is scarce, too.
The one-phonon inelastic low energy helium atom scattering theory is adapted to cases where the target monolayer is a p(1x1) commensurate square lattice. Experimental data for para-H2/NaCl(001) are re-analyzed and the relative intensities of energy loss peaks in the range 6 to 9 meV are determined. The case of the H2/NaCl(001) monolayer for 26 meV scattering energy is computationally challenging and difficult because it has a much more corrugated surface than those in the previous applications for triangular lattices. This requires a large number of coupled channels for convergence in the wave-packet-scattering calculation and a long series of Fourier amplitudes to represent the helium-target potential energy surface. A modified series is constructed in which a truncated Fourier expansion of the potential is constrained to give the exact value of the potential at some key points and which mimics the potential with fewer Fourier amplitudes. The shear horizontal phonon mode is again accessed by the helium scattering for small misalignment of the scattering plane relative to symmetry axes of the monolayer. For 1° misalignment, the calculated intensity of the longitudinal acoustic phonon mode frequently is higher than that of the shear horizontal phonon mode in contrast to what was found at scattering energies near 10 meV for triangular lattices of Ar, Kr, and Xe on Pt(111).
We present a scheme for cooling a vibrational mode of a magnetic molecular nanojunction by a spin-polarized charge current upon exploiting the interaction between its magnetic moment and the vibration. The spin-polarized charge current polarizes the magnetic moment of the nanoisland, thereby lowering its energy. A small but finite coupling between the vibration and the magnetic moment permits a direct exchange of energy such that vibrational energy can be transferred into the magnetic state. For positive bias voltages, this generates an effective cooling of the molecular vibrational mode. We determine parameter regimes for the cooling of the vibration to be optimal. Although the flowing charge current inevitably heats up the vibrational mode via Ohmic energy losses, we show that due to the magnetomechanical coupling, the vibrational energy (i.e, the effective phonon temperature) can be lowered below 50% of its initial value, when the two leads are polarized anti-parallel. In contrast to the cooling effect for positive bias voltages, net heating of the vibrational mode occurs for negative bias voltages. The cooling effect is enhanced for a stronger anti-parallel magnetic polarization of the leads, while the heating is stronger for a larger parallel polarization. Yet, dynamical cooling is also possible with parallel lead alignments when the two tunneling barriers are asymmetric.