Compositional optimization of hard-magnetic phases with machine-learning models

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of describing the different configurations enables the subsequent use of the ML models for compositional optimization and thereby the prediction of promising substitutes of state-of-the-art magnetic materials like Nd$_2$Fe$_{14}$B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements.Comment: 12 pages, 6 figure

Interplay of charge-transfer and Mott-Hubbard physics approached by an efficient combination of self-interaction correction and dynamical mean-field theory

Late transition-metal oxides with small charge-transfer energy $\Delta$ raise issues for state-of-the-art correlated electronic structure schemes such as the combination of density functional theory (DFT) with dynamical mean-field theory (DMFT). The accentuated role of the oxygen valence orbitals in these compounds asks for an enhanced description of ligand-based correlations. Utilizing the rocksalt-like NiO as an example, we present an advancement of charge self-consistent DFT+DMFT by including self-interaction correction (SIC) applied to oxygen. This introduces explicit onsite O correlations as well as an improved treatment of intersite $p-d$ correlations. Due to the efficient SIC incorporation in a pseudopotential form, the DFT+sicDMFT framework is an advanced but still versatile method to address the interplay of charge-transfer and Mott-Hubbard physics. We revisit the spectral features of stoichiometric NiO and reveal the qualitative sufficiency of local DMFT self-energies in describing spectral peak structures usually associated with explicit nonlocal processes. For Li$_x$Ni$_{1-x}$O, prominent in-gap states are verified by the present theoretical study.Comment: 8 pages, 6 figure

Learning viewpoint invariant object representations using a temporal coherence principle

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability” objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an - also unlabeled - distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformation

The necessity of a global binding framework for sustainable management of chemicals and materials — interactions with climate and biodiversity

Sustainable chemicals and materials management deals with both the risks and the opportunities of chemicals and products. It is not only focused on hazards and risks of chemicals for human health and the environment but also includes the management of material flows from extraction of raw materials up to waste. It becomes apparent meanwhile that the ever-growing material streams endanger the Earth system. According to a recent publication of Persson et al., the planetary boundaries for chemicals and plastics have already been exceeded. Therefore, sustainable chemicals and materials management must become a third pillar of international sustainability policy. For climate change and biodiversity, binding international agreements already exist. Accordingly, a global chemicals and materials framework convention integrating the current fragmented and non-binding approaches is needed. The impacts of chemicals and materials are closely related to climate change. About one third of greenhouse gas (GHG) emissions are linked to the production of chemicals, materials and products and the growing global transport of goods. Most of it is assigned to the energy demand of production and transport. GHG emissions must be reduced by an expansion of the circular economy, i.e., the use of secondary instead of primary raw materials. The chemical industry is obliged to change its feedstock since chemicals based on mineral oil and natural gas are not sustainable. Climate change in turn has consequences for the fate and effects of substances in the environment. Rising temperature implies higher vapor pressure and may enhance the release of toxicants into the atmosphere. Organisms that are already stressed may react more sensitively when exposed to toxic chemicals. The increasing frequency of extreme weather events may re-mobilize contaminants in river sediments. Increasing chemical and material load also threatens biodiversity, e.g., by the release of toxic chemicals into air, water and soil up to high amounts of waste. Fertilizers and pesticides are damaging the biocoenoses in agrarian landscapes. In order to overcome these fatal developments, sustainable management of chemicals and materials is urgently needed. This includes safe and sustainable chemicals, sustainable chemical production and sustainable materials flow management. All these three sustainability strategies are crucial and complement each other: efficiency, consistency and sufficiency. This obligates drastic changes not only of the quantities of material streams but also of the qualities of chemicals and materials in use. A significant reduction in production volumes is necessary, aiming not only to return to a safe operating space with respect to the planetary boundary for chemicals, plastics and waste but also in order to achieve goals regarding climate and biodiversity

PFAS: forever chemicals — persistent, bioaccumulative and mobile: reviewing the status and the need for their phase out and remediation of contaminated sites

Background Per- and polyfluorinated alkyl substances (PFAS) have received increasing scientific and political attention in recent years. Several thousand commercially produced compounds are used in numerous products and technical processes. Due to their extreme persistence in the environment, humans and all other life forms are, therefore, increasingly exposed to these substances. In the following review, PFAS will be examined comprehensively. Results The best studied PFAS are carboxylic and sulfonic acids with chain lengths of C4 to C14, particularly perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS). These substances are harmful to aquatic fauna, insects, and amphibians at concentrations of a few µg/L or less, accumulate in organisms, and biomagnify in food webs. Humans, as the final link in numerous food chains, are subjected to PFAS uptake primarily through food and drinking water. Several PFAS have multiple toxic effects, particularly affecting liver, kidney, thyroid, and the immune system. The latter effect is the basis for the establishment of a tolerable weekly dose of only 4.4 ng/kg body weight for the sum of the four representatives PFOA, PFOS, perfluorononanoic acid (PFNA) and perfluorohexane sulfonic acid (PFHxS) by the European Food Safety Authority (EFSA) in 2020. Exposure estimates and human biomonitoring show that this value is frequently reached, and in many cases exceeded. PFAS are a major challenge for analysis, especially of products and waste: single-substance analyses capture only a fragment of the large, diverse family of PFAS. As a consequence, sum parameters have gained increasing importance. The high mobility of per and polyfluorinated carboxylic and sulfonic acids makes soil and groundwater pollution at contaminated sites a problem. In general, short-chain PFAS are more mobile than long-chain ones. Processes for soil and groundwater purification and drinking water treatment are often ineffective and expensive. Recycling of PFAS-containing products such as paper and food packaging leads to carryover of the contaminants. Incineration requires high temperatures to completely destroy PFAS. After PFOA, PFOS and a few other perfluorinated carboxylic and sulfonic acids were regulated internationally, many manufacturers and users switched to other PFAS: short-chain representatives, per- and polyfluorinated oxo carboxylic acids, telomeric alcohols and acids. Analytical studies show an increase in environmental concentrations of these chemicals. Ultra-short PFAS (chain length C1–C3) have not been well studied. Among others, trifluoroacetic acid (TFA) is present globally in rapidly increasing concentrations. Conclusions The substitution of individual PFAS recognized as hazardous by other possibly equally hazardous PFAS with virtually unknown chronic toxicity can, therefore, not be a solution. The only answer is a switch to fluorine-free alternatives for all applications in which PFAS are not essential

SMAuC -- The Scientific Multi-Authorship Corpus

With an ever-growing number of new publications each day, scientific writing poses an interesting domain for authorship analysis of both single-author and multi-author documents. Unfortunately, most existing corpora lack either material from the science domain or the required metadata. Hence, we present SMAuC, a new metadata-rich corpus designed specifically for authorship analysis in scientific writing. With more than three million publications from various scientific disciplines, SMAuC is the largest openly available corpus for authorship analysis to date. It combines a wide and diverse range of scientific texts from the humanities and natural sciences with rich and curated metadata, including unique and carefully disambiguated author IDs. We hope SMAuC will contribute significantly to advancing the field of authorship analysis in the science domain

Learning viewpoint invariant object representations using a temporal coherence principle

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the “stability” objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells’ input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an – also unlabeled – distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations

High-Throughput Screening of Rare-Earth-Lean Intermetallic 1-13-X Compounds for Good Hard-Magnetic Properties

By computational high-throughput screening, the spontaneous magnetization Ms, uniaxial magnetocrystalline anisotropy constant K₁, anisotropy field Ha, and maximum energy product (BH)max are estimated for ferromagnetic intermetallic phases with a tetragonal 1-13-X structure related to the LaCo₉Si₄ structure type. For SmFe₁₃N, a (BH)max as high as that of Nd₂Fe₁₄B and a comparable K₁ are predicted. Further promising candidates of composition SmFe₁₂AN with A = Co, Ni, Cu, Zn, Ga, Ti, V, Al, Si, or P are identified which potentially reach (BH)max values higher than 400 kJ/m³ combined with significant K₁ values, while containing almost 50% less rare-earth atoms than Nd₂Fe₁₄B