Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance

Artificial intelligence (AI) can accelerate the design of materials by identifying correlations and complex patterns in data. However, AI methods commonly attempt to describe the entire, immense materials space with a single model, while it is typical that different mechanisms govern the materials behaviors across the materials space. The subgroup-discovery (SGD) approach identifies local rules describing exceptional subsets of data with respect to a given target. Thus, SGD can focus on mechanisms leading to exceptional performance. However, the identification of appropriate SG rules requires a careful consideration of the generality-exceptionality tradeoff. Here, we discuss challenges to advance the SGD approach in materials science and analyse the tradeoff between exceptionality and generality based on a Pareto front of SGD solutions

Identifying Outstanding Transition‑Metal‑Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery

In order to estimate the reactivity of a large number of potentially complex heterogeneous catalysts while searching for novel and more efficient materials, physical as well as data-centric models have been developed for a faster evaluation of adsorption energies compared to first-principles calculations. However, global models designed to describe as many materials as possible might overlook the very few compounds that have the appropriate adsorption properties to be suitable for a given catalytic process. Here, the subgroup-discovery (SGD) local artificial-intelligence approach is used to identify the key descriptive parameters and constrains on their values, the so-called SG rules, which particularly describe transition-metal surfaces with outstanding adsorption properties for the oxygen reduction and evolution reactions. We start from a data set of 95 oxygen adsorption energy values evaluated by density-functional-theory calculations for several monometallic surfaces along with 16 atomic, bulk and surface properties as candidate descriptive parameters. From this data set, SGD identifies constraints on the most relevant parameters describing materials and adsorption sites that (i) result in O adsorption energies within the Sabatier-optimal range required for the oxygen reduction reaction and (ii) present the largest deviations from the linear scaling relations between O and OH adsorption energies, which limit the performance in the oxygen evolution reaction. The SG rules not only reflect the local underlying physicochemical phenomena that result in the desired adsorption properties but also guide the challenging design of alloy catalysts

The relation between the number of symptoms and other health indicators in working men and women

Simple symptom counts are widely used Indicators of ill-health in health survey research. However, there has only been little research aimed at a better understanding of symptom counts. The purpose of this study was to explore the number of symptoms (a summary score derived from 10 unspecific self-reported symptoms) in a ‘healthy' population. We reanalysed data on a sample of 850 employed men and women (Berne Workplace Health Project). Stepwise ordinal logistic regression analysis was used to study the relation of the number of symptoms to contextual, person-related, behavioural, and health-related variables. In neither men nor women were any of the sociodemographic Indicators significantly associated with the number of symptoms. Of the work-related variables, low job discretion and adverse work environment were significantly associated with the number of symptoms and there was a statistical trend for high job demands. In women, dissatisfaction with salary was the only work-related variable which was significant. The private context, on the other hand, seemed to be more influential for women than for men and there was a statistical trend for strain at home. Among the person-related factors studied, age and emotional problems were significantly associated to the number of symptoms in both men and women. Psychological ill-health and negative future orientation were significant only in men and immersion only in women. Physical inactivity in leisure time was the only behavioural variable found to be significantly associated to the number of symptoms. Among the health-related factors, poor self-rated health was significant for both gender groups, while obesity was significant only in men. Only categories of variables being hypothesized to be unfavourable in terms of health were associated with a higher number of symptoms. Most of the associations found seem to be theoretically meaningful. Despite methodological provisos, our findings support a conception of the number of symptoms as an indicator of general ill-healt

Identifying outstanding transition-metal-alloy heterogeneous catalysts for the oxygen reduction and evolution reactions via subgroup discovery

In order to estimate the reactivity of a large number of potentially complex heterogeneous catalysts while searching for novel and more efficient materials, physical as well as data-centric models have been developed for a faster evaluation of adsorption energies compared to first-principles calculations. However, global models designed to describe as many materials as possible might overlook the very few compounds that have the appropriate adsorption properties to be suitable for a given catalytic process. Here, the subgroup-discovery (SGD) local artificial-intelligence approach is used to identify the key descriptive parameters and constrains on their values, the so-called SG rules, which particularly describe transition-metal surfaces with outstanding adsorption properties for the oxygen reduction and evolution reactions. We start from a data set of 95 oxygen adsorption energy values evaluated by density-functional-theory calculations for several monometallic surfaces along with 16 atomic, bulk and surface properties as candidate descriptive parameters. From this data set, SGD identifies constraints on the most relevant parameters describing materials and adsorption sites that (i) result in O adsorption energies within the Sabatier-optimal range required for the oxygen reduction reaction and (ii) present the largest deviations from the linear scaling relations between O and OH adsorption energies, which limit the performance in the oxygen evolution reaction. The SG rules not only reflect the local underlying physicochemical phenomena that result in the desired adsorption properties but also guide the challenging design of alloy catalysts

Human papillomavirus viral load: a possible marker for cervical disease in HIV-infected women

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