cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:mx-3a
title: A unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials

description: Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel treatment for the frequency dependence, introducing an algorithmic inversion method that can be applied to dynamical potentials expanded as sum-over-poles. This approach allows for an exact solution of Dyson-like equations at all frequencies via a mapping to a matrix diagonalization, and provides simultaneously frequency-dependent (spectral) and frequency-integrated (thermodynamic) properties of the Dyson-inverted propagators.
The transformation to a sum-over-poles is performed introducing n-th order generalized Lorentzians as an improved basis set to represent the spectral function of a propagator.
Numerical results for the homogeneous electron gas at the G0W0 level are provided to argue for the accuracy and efficiency of such unified approach.
In this record, we provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.
DOI: 10.24435/materialscloud:56-6p
title: A mobile soil analysis system for sustainable agriculture

description: For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97.8% of cases (132 out of 135 tests) while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.
This record comprises a data set of approximately 800 images of a colorimetric paper-based chemical analysis device captured with a custom mobile application at outdoor conditions. The data set also contains three csv files collecting the color information (RGB) values and analysis results (pH values) as determined by the mobile application models from those paper-based device images. These csv files correspond to the RGB data and associated pH values as captured on the field (pre-processed), after processing post field test and a set corresponding to the measurements on a compound sample combining the 9 soil samples collected per hectare. 
An additional data set is included corresponding to the images and RGB data used for the calibration of the logistic regression models used by the mobile application to predict pH values from the colorimetric information.  
Python code is made available for the analysis of the colorimetric chemical reaction on paper of chemical reagents to soil samples across a range of acidity values, including the application of an illumination compensation method, and for the training of predictive machine learning models.
DOI: 10.24435/materialscloud:9w-yv
title: A unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials

description: Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel treatment for the frequency dependence, introducing an algorithmic inversion method that can be applied to dynamical potentials expanded as sum-over-poles. This approach allows for an exact solution of Dyson-like equations at all frequencies via a mapping to a matrix diagonalization, and provides simultaneously frequency-dependent (spectral) and frequency-integrated (thermodynamic) properties of the Dyson-inverted propagators.
The transformation to a sum-over-poles is performed introducing n-th order generalized Lorentzians as an improved basis set to represent the spectral function of a propagator.
Numerical results for the homogeneous electron gas at the G0W0 level are provided to argue for the accuracy and efficiency of such unified approach.
In this record, we provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.
DOI: 10.24435/materialscloud:9w-gz
title: On the effects of the degrees of freedom on calculating diffusion properties in nanoporous materials

description: If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved and hence the number of degrees of freedom is set to 3N-3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature.
For sufficiently large systems, the difference between 3N and 3N-3 is negligible in the way. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counter-intuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion, is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results.
Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero loading conditions is introduced.
DOI: 10.24435/materialscloud:p1-2y
title: A data-science approach to predict the heat capacity of nanoporous materials

description: The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the energy requirement is reduced by as much as a factor of two using the correct heat capacity.
DOI: 10.24435/materialscloud:hr-kn
title: A mobile soil analysis system for sustainable agriculture

description: For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97.8% of cases (132 out of 135 tests) while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.
This record comprises a data set of approximately 800 images of a colorimetric paper-based chemical analysis device captured with a custom mobile application at outdoor conditions. The data set also contains three csv files collecting the color information (RGB) values and analysis results (pH values) as determined by the mobile application models from those paper-based device images. These csv files correspond to the RGB data and associated pH values as captured on the field (pre-processed), after processing post field test and a set corresponding to the measurements on a compound sample combining the 9 soil samples collected per hectare. 
An additional data set is included corresponding to the images and RGB data used for the calibration of the logistic regression models used by the mobile application to predict pH values from the colorimetric information.  
Python code is made available for the analysis of the colorimetric chemical reaction on paper of chemical reagents to soil samples across a range of acidity values, including the application of an illumination compensation method, and for the training of predictive machine learning models.
DOI: 10.24435/materialscloud:tv-ja
title: A data-science approach to predict the heat capacity of nanoporous materials

description: The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the energy requirement is reduced by as much as a factor of two using the correct heat capacity.
DOI: 10.24435/materialscloud:c6-39
title: Artificial intelligence enables mobile soil analysis for sustainable agriculture

description: For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97.8% of cases (132 out of 135 tests) while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.
This record comprises a data set of approximately 800 images of a colorimetric paper-based chemical analysis device captured with a custom mobile application at outdoor conditions. The data set also contains three csv files collecting the color information (RGB) values and analysis results (pH values) as determined by the mobile application models from those paper-based device images. These csv files correspond to the RGB data and associated pH values as captured on the field (pre-processed), after processing post field test and a set corresponding to the measurements on a compound sample combining the 9 soil samples collected per hectare. 
An additional data set is included corresponding to the images and RGB data used for the calibration of the logistic regression models used by the mobile application to predict pH values from the colorimetric information.  
Python code is made available for the analysis of the colorimetric chemical reaction on paper of chemical reagents to soil samples across a range of acidity values, including the application of an illumination compensation method, and for the training of predictive machine learning models.
DOI: 10.24435/materialscloud:vt-4t
title: Artificial intelligence enables mobile soil analysis for sustainable agriculture

description: For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97% of cases while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.
This record comprises a data set of approximately 800 images of a colorimetric paper-based chemical analysis device captured with a custom mobile application at outdoor conditions. The data set also contains three csv files collecting the color information (RGB) values and analysis results (pH values) as determined by the mobile application models from those paper-based device images. These csv files correspond to the RGB data and associated pH values as captured on the field (pre-processed), after processing post field test and a set corresponding to the measurements on a compound sample combining the 9 soil samples collected per hectare. 
An additional data set is included corresponding to the images and RGB data used for the calibration of the logistic regression models used by the mobile application to predict pH values from the colorimetric information.  
Python code is made available for the analysis of the colorimetric chemical reaction on paper of chemical reagents to soil samples across a range of acidity values, including the application of an illumination compensation method, and for the training of predictive machine learning models.
DOI: 10.24435/materialscloud:5a-vm
title: Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches

description: Li–S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulphide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li–S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe–N4–C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyse the adsorption trends on Zn–N4–C SAC and observe similar adsorption strength and behaviour with the Fe–N4–C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption
DOI: 10.24435/materialscloud:n2-tg
title: Artificial intelligence enables mobile soil analysis for sustainable agriculture

description: For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97% of cases while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.
This record comprises a data set of approximately 800 images of a colorimetric paper-based chemical analysis device captured with a custom mobile application at outdoor conditions. The data set also contains three csv files collecting the color information (RGB) values and analysis results (pH values) as determined by the mobile application models from those paper-based device images. These csv files correspond to the RGB data and associated pH values as captured on the field (pre-processed), after processing post field test and a set corresponding to the measurements on a compound sample combining the 9 soil samples collected per hectare. 
An additional data set is included corresponding to the images and RGB data used for the calibration of the logistic regression models used by the mobile application to predict pH values from the colorimetric information.  
Python code is made available for the analysis of the colorimetric chemical reaction on paper of chemical reagents to soil samples across a range of acidity values, including the application of an illumination compensation method, and for the training of predictive machine learning models.
DOI: 10.5281/zenodo.15726136
title: Design of Borole and Silylene-Based Frustrated Lewis Pairs for Dinitrogen Activation Promoted by Aromaticity: A Combined DFT and Machine Learning Study
DOI: 10.24435/materialscloud:py-tv
title: Quantum phase diagram of high-pressure hydrogen

description: The interplay between electron correlation and nuclear quantum effects makes our understanding of elemental hydrogen a formidable challenge. Here, we present the phase diagram of hydrogen and deuterium at low temperatures and high-pressure (P > 300 GPa) by accounting for highly accurate electronic and nuclear enthalpies. We evaluated internal electronic energies by diffusion quantum Monte Carlo, while nuclear quantum motion and anharmonicity have been included by the stochastic self-consistent harmonic approximation. Our results show that the long-sought atomic metallic hydrogen, predicted to host room-temperature superconductivity, forms at 577±10 GPa (640±14 GPa in deuterium). Indeed, anharmonicity pushes the stability of this phase towards pressures much larger than previous theoretical estimates or attained experimental values. Before atomization, molecular hydrogen transforms from a metallic phase III to another metallic structure that is still molecular (phase VI) at 422±40 GPa (442±30 GPa in deuterium). We predict clear-cut signatures in optical spectroscopy and DC conductivity that can be used experimentally to distinguish between the two structural transitions. According to our findings, the experimental evidence of metallic hydrogen has so far been limited to molecular phases.
The data uploaded here contains the structural information of the simulated phases of hydrogen and their quantum centroid position as a function of pressure and isotope.