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

Direito à educação brasileiro e as capacitações para um ideal de democracia deliberativa

O presente trabalho apresenta o direito à educação brasileiro como um modo de se alcançar um ideal de democracia deliberativa. O ideal propõe decisões mais legítimas, em termos de razão pública, e um senso de responsividade por parte dos representantes. Isso garante que todos cidadãos sejam tratados com igual respeito e consideração. A partir da Abordagem das Capacitações, define-se que, para atingir aquele ideal, são necessários o desenvolvimento de capacitações relacionadas à argumentação, ao pensamento crítico, à retórica e à compaixão. Essas capacitações permitem uma argumentação em termos endossáveis por todos e também uma disposição de ouvir e de se importar com as reivindicações de outras pessoas. O direito à educação brasileiro, como um funcionamento fértil, possui diversas ferramentas para desenvolver essas capacitações – por exemplo: os debates críticos em sala de aula e o ensino de Literatura e outras artes. Consequentemente, a efetivação do direito à educação brasileiro é essencial para tornar o ordenamento jurídico do país mais legítimo.This work presents Brazilian right to education as a way to reach an ideal of deliberative democracy. The ideal proposes more legitm decisions, in terms of public reason, and a sense of accountability by the representatives. This grants that all citizens be treated with equal respect and consideration. Based on Capabilities Approach, it is defined that, in order to achieve that ideal, the development of capabilitiesrelated to argumentation, to critical thinking, to rethoric and to compassion is necessary. Those capabilities enables an argumentation in terms endossable for all and also a willingness to listen and to care about the claims of other people. The Brazilian right to education, as a fertile functioning, has a lot of tools to develop this capabilities – for exemple: the classroom critical debates and the teaching of Literature and other arts. Therefore, the effecting of Brazilian right to education is essential to make the country’s legal order more legitim

Lógica, Direito e Educação: uma análise da estrutura lógico-jurídica do RE 888.815

Resumo SIC - [ógica, Direito e Educação: uma análise da estrutura lógico-jurídica do RE 888.815

Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data

Designing materials for catalysis is challenging because the performance is governed by an intricate interplay of various multiscale phenomena, such as the chemical reactions on surfaces and the materials’ restructuring during the catalytic process. In the case of supported catalysts, the role of the support material can be also crucial. Here, we address this intricacy challenge by a symbolic-regression artificial intelligence (AI) approach. We identify the key physicochemical parameters correlated with the measured performance, out of many offered candidate parameters characterizing the materials, reaction environment, and possibly relevant underlying phenomena. Importantly, these parameters are obtained by both experiments and ab initio simulations. The identified key parameters might be called “materials genes”, in analogy to genes in biology: they correlate with the property or function of interest, but the explicit physical relationship is not (necessarily) known. To demonstrate the approach, we investigate the CO2 hydrogenation catalyzed by cobalt nanoparticles supported on silica. Crucially, the silica support is modified with the additive metals magnesium, calcium, titanium, aluminum, or zirconium, which results in six materials with significantly different performances. These systems mimic hydrothermal vents, which might have produced the first organic molecules on Earth. The key parameters correlated with the CH3OH selectivity reflect the reducibility of cobalt species, the adsorption strength of reaction intermediates, and the chemical nature of the additive metal. By using an AI model trained on basic elemental properties of the additive metals (e.g., ionization potential) as physicochemical parameters, new additives are suggested. The predicted CH3OH selectivity of cobalt catalysts supported on silica modified with vanadium and zinc is confirmed by new experiments., Deutsche Forschungsgemeinschaft 10.13039/501100001659Max-Planck-Gesellschaft 10.13039/501100004189Max Planck-Cardiff Centre on the Fundamentals of Heterogeneous Catalysis NAVolkswagen Foundation 10.13039/501100001663Horizon 2020 Framework Programme 10.13039/100010661Peer Reviewe

From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery

Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-informed acquisition function--with the goal to maximize a certain "reward" over time, such as the maximum property value discovered so far. While the materials science community achieved much progress in developing property models that predict well on average with respect to the training distribution, this form of in-distribution performance measurement is not directly coupled with the discovery reward. This is because an iterative discovery process has a shifting reward distribution that is over-proportionally determined by the model performance for exceptional materials. We demonstrate this problem using the example of bulk modulus maximization among double perovskite oxides. We find that the in-distribution predictive performance suggests random forests as superior to Gaussian process regression, while the results are inverse in terms of the discovery rewards. We argue that the lack of proper performance estimation methods from pre-computed data collections is a fundamental problem for improving data-driven materials discovery, and we propose a novel such estimator that, in contrast to na\"ive reward estimation, successfully predicts Gaussian processes with the "expected improvement" acquisition function as the best out of four options in our demonstrational study for double perovskites. Importantly, it does so without requiring the over thousand ab initio computations that were needed to confirm this prediction.Comment: Simplified notatio