Improving Molecular Force Fields Across Configurational Space by Combining Supervised and Unsupervised Machine Learning

The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), thus choosing the training set randomly or according to the probability distribution of the data leads to models whose accuracy is mainly defined by the most common close-to-equilibrium configurations in the reference data. In this work, we combine unsupervised and supervised ML methods to bypass the inherent bias of the data for common configurations, effectively widening the applicability range of MLFF to the fullest capabilities of the dataset. To achieve this goal, we first cluster the CS into subregions similar in terms of geometry and energetics. We iteratively test a given MLFF performance on each subregion and fill the training set of the model with the representatives of the most inaccurate parts of the CS. The proposed approach has been applied to a set of small organic molecules and alanine tetrapeptide, demonstrating an up to two-fold decrease in the root mean squared errors for force predictions of these molecules. This result holds for both kernel-based methods (sGDML and GAP/SOAP models) and deep neural networks (SchNet model). For the latter, the developed approach simultaneously improves both energy and forces, bypassing the compromise to be made when employing mixed energy/force loss functions

Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules

Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van der Waals interactions. This interplay between interactions yields complex potential-energy surfaces (PES) with multiple minima and transition paths between them. In this work, we assess the performance of state-of-the-art Machine Learning (ML) models, namely sGDML, SchNet, GAP/SOAP, and BPNN for reproducing such PES, while using limited amounts of reference data. As a benchmark, we use the cis to trans thermal relaxation in an azobenzene molecule, where at least three different transition mechanisms should be considered. Although GAP/SOAP, SchNet, and sGDML models can globally achieve chemical accuracy of 1 kcal mol-1 with fewer than 1000 training points, predictions greatly depend on the ML method used as well as the local region of the PES being sampled. Within a given ML method, large differences can be found between predictions of close-to-equilibrium and transition regions, as well as for different transition mechanisms. We identify key challenges that the ML models face in learning long-range interactions and the intrinsic limitations of commonly used atom-based descriptors. All in all, our results suggest switching from learning the entire PES within a single model to using multiple local models with optimized descriptors, training sets, and architectures for different parts of complex PES

Holacracy and Obliquity: contingency management approaches in organizing companies

The paper aims to examine the development of modern innovative management methods and practices such as Holacracy, Obliquity, Adhocracy and Sociocracy, which are novelty in the Management science and practice. The study illustrates contingency approaches in designing, managing and developing agile companies from wide varieties of industries. The paper sheds light on contemporary methods in organizing, planning and setting goals of companies in a post-knowledge era. It is like an operating system for business that requires the installation of different applications as applications for hiring employees, for setting salaries, for planning or logistics. In the paper, literature review on management innovations is conducted and subsequently statistical operationalization through STATA software has been employed to examine how particular organizations design and set up their organizational structures such as lean, agile or scrum. Paper results show that smaller companies are more agile and they tend to acquire Holacratic Management models thanks to the fact that self-managing teams exist internally and their organizational structures are flatter and more adaptive in comparison to the multinational corporations. Consequently, the paper concludes with suggestions on innovative management implementations for future development of companies and emphasizes the need for further research on what is the impact of Holacracy and Obliquity on shaping the organizational culture of companies

Comparing the performance of fluidized and fixed granular activated carbon beds as cathodes for microbial electrosynthesis of carboxylates from CO2

Microbial electrosynthesis (MES) can use renewable electricity to power microbial conversion of carbon dioxide (CO2) into carboxylates. To ensure high productivities in MES, good mass transfer must be ensured, which could be accomplished with fluidization of granular activated carbon (GAC). In this study, fluidized and fixed GAC bed cathodes were compared. Acetate production rate and current density were 42 % and 47 % lower, respectively, in fluidized than fixed bed reactors. Although similar microbial consortium dominated by Eubacterium and Proteiniphilum was observed, lowest biomass quantity was measured with fixed GAC bed indicating higher specific acetate production rates compared to fluidized GAC bed. Furthermore, charge efficiency was the highest and charge recovery in carboxylates the lowest in fixed GAC beds indicating enhanced hydrogen evolution and need for enhancing CO2 feeding to enable higher production rates of acetate. Overall, fixed GAC beds have higher efficiency for acetate production in MES than fluidized GAC beds.Peer reviewe

Cathodic biofilms – A prerequisite for microbial electrosynthesis

Cathodic biofilms have an important role in CO2 bio-reduction to carboxylic acids and biofuels in microbial electrosynthesis (MES) cells. However, robust and resilient electroactive biofilms for an efficient CO2 conversion are difficult to achieve. In this review, the fundamentals of cathodic biofilm formation, including energy conservation, electron transfer and development of catalytic biofilms, are presented. In addition, strategies for improving cathodic biofilm formation, such as the selection of electrode and carrier materials, cell design and operational conditions, are described. The knowledge gaps are individuated, and possible solutions are proposed to achieve stable and productive biofilms in MES cathodes.publishedVersionPeer reviewe

A Swedish heterodyne facility instrument for the APEX telescope

In March 2008, the APEX facility instrument was installed on the telescope at the site of Lliano Chajnantor in northern Chile. The main objective of the paper is to introduce the new instrument to the radio astronomical community. It describes the hardware configuration and presents some initial results from the on-sky commissioning. The heterodyne instrument covers frequencies between 211 GHz and 1390 GHz divided into four bands. The first three bands are sideband-separating mixers operating in a single sideband mode and based on superconductor-insulator-superconductor (SIS) tunnel junctions. The fourth band is a hot-electron bolometer, waveguide balanced mixer. All bands are integrated in a closedcycle temperature-stabilized cryostat and are cooled to 4 K. We present results from noise temperature, sideband separation ratios, beam, and stability measurements performed on the telescope as a part of the receiver technical commissioning. Examples of broad extragalactic lines are also included

p53 Plays a Role in Mesenchymal Differentiation Programs, in a Cell Fate Dependent Manner

Background: The tumor suppressor p53 is an important regulator that controls various cellular networks, including cell differentiation. Interestingly, some studies suggest that p53 facilitates cell differentiation, whereas others claim that it suppresses differentiation. Therefore, it is critical to evaluate whether this inconsistency represents an authentic differential p53 activity manifested in the various differentiation programs. Methodology/Principal Findings: To clarify this important issue, we conducted a comparative study of several mesenchymal differentiation programs. The effects of p53 knockdown or enhanced activity were analyzed in mouse and human mesenchymal cells, representing various stages of several differentiation programs. We found that p53 downregulated the expression of master differentiation-inducing transcription factors, thereby inhibiting osteogenic, adipogenic and smooth muscle differentiation of multiple mesenchymal cell types. In contrast, p53 is essential for skeletal muscle differentiation and osteogenic re-programming of skeletal muscle committed cells. Conclusions: These comparative studies suggest that, depending on the specific cell type and the specific differentiatio

EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats

Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location: Europe. Methods: We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives