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

Late adoptions:Attachment security and emotional availability in mother-child and father-child dyads

A growing body of research suggests that a history of neglect, abuse and institutionalization can negatively affect late-adopted children's attachment representations, and that adoptive parents can play a key role in enabling adopted children to earn secure attachments. Still, only a few studies have explored the quality of caregiver-child interaction in adoptive families. The present study aimed at verifying both the concordance of attachment in adoptive dyads (mother-children and father-children) and the relationship between attachment representations and parent-child interaction. The research involved 20 adoptive families in which the child's arrival had occurred between 12 to 36 months before the assessment, and where children were aged between 4.5 and 8.5 years. Attachment was assessed through the Adult Attachment Interview for parents and through the Manchester Child Attachment Story Task for children. The emotional quality of parent-child interaction was assessed trough the Emotional Availability Scales. Our results pointed out the presence of a relation between attachment representations of late-adopted children and their adoptive mothers (75%, K = 0.50, p =.025). In addition, we found that both insecure children and mothers showed lower levels of EA than secure ones. Some explanations are presented about why, in the early post-adoption period, child attachment patterns and dyadic emotional availability seem to be arranged on different frameworks for the two parental 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

Geostatistical analysis of three dimensional current patterns in coastal oceanography: application to the Gulf of Lions (nw mediterranean sea)

International audienc