scholarly journals TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes

2021 ◽  
Author(s):  
Filipe Teixeira ◽  
Edgar Silva-Santos ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Transition Metal Complexes ◽  
Energy Production ◽  
Metal Center ◽  
Data Driven ◽  
Topological Descriptors ◽  
Data Set ◽  
Topological Features ◽  
And Storage ◽  
Optimized Geometries ◽  
Quantum Geometries

In this work, we report the TMtopo data set containing optimized geometries, quantum calculated properties, and quantum topological descriptors for 1110 first row TM complexes. Properties were computed at the TPSSh/Def2-TZVP level of theory and the quantum topological descriptors were collected under the framework of the Quantum Theory of Atoms in Molecules (QTAIM), including a systematic topological survey of the Laplacian of the electron density, ∇<sup>2</sup>ρ(r). This survey yielded novel insights on the proliferation of inner Valence Shell Charge Concentrations (iVSCCs, local minima of ∇<sup>2</sup>ρ(r)) in the metal center, suggesting that their number is determinant for the stabilization of the metal center in a more intense manner than their arrangement opposing each of the metal’s ligands (<i>Inorg. Chem.</i> 2016, <b>55</b>, 3653). Pairwise representation of the collected properties revealed overall low correlation, although some structure could be perceived in the data (specially when considering the topological features of ∇ 2 ρ(r)). This suggests that the TMtopo data set could be usefully exploited in the data-driven discovery of new TM complexes with interesting properties for applications in as catalysis, opto-electronics and sustainable energy production and storage.

scholarly journals TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes

2021 ◽  
Author(s):  
Filipe Teixeira ◽  
Edgar Silva-Santos ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Transition Metal Complexes ◽  
Energy Production ◽  
Metal Center ◽  
Data Driven ◽  
Topological Descriptors ◽  
Data Set ◽  
Topological Features ◽  
And Storage ◽  
Optimized Geometries ◽  
Quantum Geometries

In this work, we report the TMtopo data set containing optimized geometries, quantum calculated properties, and quantum topological descriptors for 1110 first row TM complexes. Properties were computed at the TPSSh/Def2-TZVP level of theory and the quantum topological descriptors were collected under the framework of the Quantum Theory of Atoms in Molecules (QTAIM), including a systematic topological survey of the Laplacian of the electron density, ∇<sup>2</sup>ρ(r). This survey yielded novel insights on the proliferation of inner Valence Shell Charge Concentrations (iVSCCs, local minima of ∇<sup>2</sup>ρ(r)) in the metal center, suggesting that their number is determinant for the stabilization of the metal center in a more intense manner than their arrangement opposing each of the metal’s ligands (<i>Inorg. Chem.</i> 2016, <b>55</b>, 3653). Pairwise representation of the collected properties revealed overall low correlation, although some structure could be perceived in the data (specially when considering the topological features of ∇ 2 ρ(r)). This suggests that the TMtopo data set could be usefully exploited in the data-driven discovery of new TM complexes with interesting properties for applications in as catalysis, opto-electronics and sustainable energy production and storage.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

scholarly journals Diffuse approximation for identification of the mechanical properties of microcapsules

2020 ◽  
pp. 108128652097760
Author(s):  
Carlos Quesada ◽  
Claire Dupont ◽  
Pierre Villon ◽  
Anne-Virginie Salsac
Keyword(s):  
Mechanical Properties ◽  
Active Material ◽  
Liquid Core ◽  
Constitutive Law ◽  
Data Driven ◽  
Membrane Properties ◽  
Mechanical Resistance ◽  
Data Set ◽  
Diffuse Approximation ◽  
Capsule Size

A novel data-driven real-time procedure based on diffuse approximation is proposed to characterize the mechanical behavior of liquid-core microcapsules from their deformed shape and identify the mechanical properties of the submicron-thick membrane that protects the inner core through inverse analysis. The method first involves experimentally acquiring the deformed shape that a given microcapsule takes at steady state when it flows through a microfluidic microchannel of comparable cross-sectional size. From the mid-plane capsule profile, we deduce two characteristic geometric quantities that uniquely characterize the shape taken by the microcapsule under external hydrodynamic stresses. To identify the values of the unknown rigidity of the membrane and of the size of the capsule, we compare the geometric quantities with the values predicted numerically using a fluid-structure-interaction model by solving the three-dimensional capsule-flow interactions. The complete numerical data set is obtained off-line by systematically varying the governing parameters of the problem, i.e. the capsule-to-tube confinement ratio, and the capillary number, which is the ratio of the viscous to elastic forces. We show that diffuse approximation efficiently estimates the unknown mechanical resistance of the capsule membrane. We validate the data-driven procedure by applying it to the geometric and mechanical characterization of ovalbumin microcapsules (diameter of the order of a few tens of microns). As soon as the capsule is sufficiently deformed to exhibit a parachute shape at the rear, the capsule size and surface shear modulus are determined with an accuracy of 0.2% and 2.7%, respectively, as compared with 2–3% and 25% without it, in the best cases (Hu et al. Characterizing the membrane properties of capsules flowing in a square-section microfluidic channel: Effects of the membrane constitutive law. Phys Rev E 2013; 87(6): 063008). Diffuse approximation thus allows the capsule size and membrane elastic resistance to be provided quasi-instantly with very high precision. This opens interesting perspectives for industrial applications that require tight control of the capsule mechanical properties in order to secure their behavior when they transport active material.

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge

2021 ◽  
Author(s):  
Xiaolong Guo ◽  
Yugang Yu ◽  
Gad Allon ◽  
Meiyan Wang ◽  
Zhentai Zhang
Keyword(s):  
Operations Management ◽  
Service Operations ◽  
Optimization Techniques ◽  
Data Driven ◽  
Analytical Models ◽  
Data Set ◽  
Service Operations Management ◽  
Level Data ◽  
Home Appliance ◽  
Research Challenge

To support the 2021 Manufacturing & Service Operations Management (MSOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers.

A New Uncertainty Analysis-Based Framework for Data-Driven Computational Mechanics

2021 ◽  
pp. 1-22
Author(s):  
Xu Guo ◽  
Zongliang Du ◽  
Chang Liu ◽  
Shan Tang
Keyword(s):  
Phase Space ◽  
Uncertainty Analysis ◽  
Distinctive Feature ◽  
Computational Mechanics ◽  
Problem Formulation ◽  
Solution Set ◽  
Data Driven ◽  
Data Set ◽  
Classical Counterpart ◽  
Single Solution

Abstract In the present paper, a new uncertainty analysis-based framework for data-driven computational mechanics (DDCM) is established. Compared with its practical classical counterpart, the distinctive feature of this framework is that uncertainty analysis is introduced into the corresponding problem formulation explicitly. Instated of only focusing on a single solution in phase space, a solution set is sought for in order to account for the influence of the multi-source uncertainties associated with the data set on the data-driven solutions. An illustrative example provided shows that the proposed framework is not only conceptually new, but also has the potential of circumventing the intrinsic numerical difficulties pertaining to the classical DDCM framework.

scholarly journals Transport and storage of anthropogenic C in the North Atlantic Subpolar Ocean

2018 ◽  
Vol 15 (14) ◽  
pp. 4661-4682 ◽  
Author(s):  
Virginie Racapé ◽  
Patricia Zunino ◽  
Herlé Mercier ◽  
Pascale Lherminier ◽  
Laurent Bopp ◽  
...  
Keyword(s):  
Interannual Variability ◽  
North Atlantic ◽  
General Circulation ◽  
Meridional Overturning Circulation ◽  
Intermediate Water ◽  
Data Set ◽  
The North ◽  
Storage Rate ◽  
The North Atlantic ◽  
And Storage

Abstract. The North Atlantic Ocean is a major sink region for atmospheric CO2 and contributes to the storage of anthropogenic carbon (Cant). While there is general agreement that the intensity of the meridional overturning circulation (MOC) modulates uptake, transport and storage of Cant in the North Atlantic Subpolar Ocean, processes controlling their recent variability and evolution over the 21st century remain uncertain. This study investigates the relationship between transport, air–sea flux and storage rate of Cant in the North Atlantic Subpolar Ocean over the past 53 years. Its relies on the combined analysis of a multiannual in situ data set and outputs from a global biogeochemical ocean general circulation model (NEMO–PISCES) at 1∕2∘ spatial resolution forced by an atmospheric reanalysis. Despite an underestimation of Cant transport and an overestimation of anthropogenic air–sea CO2 flux in the model, the interannual variability of the regional Cant storage rate and its driving processes were well simulated by the model. Analysis of the multi-decadal simulation revealed that the MOC intensity variability was the major driver of the Cant transport variability at 25 and 36∘ N, but not at OVIDE. At the subpolar OVIDE section, the interannual variability of Cant transport was controlled by the accumulation of Cant in the MOC upper limb. At multi-decadal timescales, long-term changes in the North Atlantic storage rate of Cant were driven by the increase in air–sea fluxes of anthropogenic CO2. North Atlantic Central Water played a key role for storing Cant in the upper layer of the subtropical region and for supplying Cant to Intermediate Water and North Atlantic Deep Water. The transfer of Cant from surface to deep waters occurred mainly north of the OVIDE section. Most of the Cant transferred to the deep ocean was stored in the subpolar region, while the remainder was exported to the subtropical gyre within the lower MOC.

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