Evaluating Charge Equilibration Methods To Generate Electrostatic Fields in Nanoporous Materials

Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in metal-organic frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely, CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges, and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

Metal-organic frameworks as kinetic modulators for branched selectivity in hydroformylation.

Finding heterogeneous catalysts that are superior to homogeneous ones for selective catalytic transformations is a major challenge in catalysis. Here, we show how micropores in metal-organic frameworks (MOFs) push homogeneous catalytic reactions into kinetic regimes inaccessible under standard conditions. Such property allows branched selectivity up to 90% in the Co-catalysed hydroformylation of olefins without directing groups, not achievable with existing catalysts. This finding has a big potential in the production of aldehydes for the fine chemical industry. Monte Carlo and density functional theory simulations combined with kinetic models show that the micropores of MOFs with UMCM-1 and MOF-74 topologies increase the olefins density beyond neat conditions while partially preventing the adsorption of syngas leading to high branched selectivity. The easy experimental protocol and the chemical and structural flexibility of MOFs will attract the interest of the fine chemical industries towards the design of heterogeneous processes with exceptional selectivity

Evolution of the road and rail transport of goods in european countries before and after the financial crises

The main goal of this paper is to analyse recent trends in freight transport volumes as well as their relation to socio-economic and infrastructural variables, in the case of the following major European countries: France, Germany, Italy, Poland, Spain and the United Kingdom. This analysis refers to the period 2005-2016, so that years affected by the global economic crisis, which shows its peak in 2009, are taken into account. This research demonstrated that not all the countries under study show a strong relation between freight traffic and GDP as it could have been expected based on well consolidated experiences and studies. Moreover, other relations are investigated, with mixed results, between the freight traffic volumes and the extension of the rail and road networks as well as oil price data

A Chemical Approach to Biomass Gasification

This paper describes GASDS, a comprehensive model of a biomass gasifier on a travelling grate. This general mathematical model deals with the complex multiphase gas¬solid problem of biomass gasification both at the reactor and the particle scale. Mass and energy balances plus proper closure equations describe the behavior of both the phases. Key elements of the model are the detailed kinetic schemes describing the biomass volatilization, including the formation of TAR species and char, and the secondary gas phase as well as the heterogeneous reactions. The model has been already tested and validated on semi¬quantitative basis in comparisons with a 12 MW biomass combustor operating in Belgium. In this paper, the model is extended and compared to recent experimental data obtained in a biomass regenerative gasifier. The comparison with experimental data highlights the role of devolatilization, mass transfer phenomena, and mainly the kinetics of char gasification in controlling system's reactivity and syngas composition. An alternative configuration for biomass gasification is also presented and discussed. It is based on three different steps, including biomass pyrolysis through the convective heating of a syngas recycle stream, followed by the oxidation of volatile products (gas and tar compounds) with an air/steam mixture and a final char gasification. The advantage of the proposed configuration lies in the direct oxidation of the released tars to form the hot stream used for char gasification