Despite being the archetypal thermoelectric material, still today some of the most exciting advances in the efficiency of these materials are being achieved by tuning the properties of PbTe. Its inherently low lattice thermal conductivity can be lowered to its fundamental limit by designing a structure capable of scattering phonons over a wide range of length scales. Intrinsic defects, such as vacancies or grain boundaries, can and do play the role of these scattering sites. Here we assess the effect of these defects by means of molecular dynamics simulations. For this we purposely parametrize a Buckingham potential that provides an excellent description of the thermal conductivity of this material over a wide temperature range. Our results show that intrinsic point defects and grain boundaries can reduce the lattice conductivity of PbTe down to a quarter of its bulk value. By studying the size dependence we also show that typical defect concentrations and grain sizes realized in experiments normally correspond to the bulk lattice conductivity of pristine PbTe.

The lithium-ion battery (LIB) research literature has increased very rapidly of late. While this is an immense source of valuable knowledge and facts for the community, these are also partly "buried" in the literature. To truly make the most possible use of the information available and automate "reading", special tools are required. Named entity recognition (NER) is one such tool, which uses supervised machine learning for information extraction. To enable efficient NER, however, a large and highquality annotated corpus is crucial. Here, we report on our generated, semi-automatically annotated lithium-ion battery annotated corpus, "LIBAC", for 28 different entities of LIBs, which was used for training and evaluating Tok2vec and Transformer-based models, resulting in high general accuracies for these with F 1 -scores of 81 and 83%, respectively. LIBAC itself was created from 6985 paragraphs randomly chosen from ca. 11,000 LIB research papers and contains 73,300 annotated spans (627,428 tokens). This is the prime stepping-stone needed to develop a large-scale information extraction system designed for the LIB research literature.

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Magnesium (Mg) is one of the most abundant metallic elements in nature and presents attractive mechanical properties in the industry. Particularly, it has a low density and relatively high strength/weight and stiffness/weight ratios, which make it one of the most attractive lightweight metals. However, the huge potential of Mg is restricted by its low ductility, associated with its hexagonal close packed (hcp) structure. This problem can be solved if Mg adopts the body centered cubic (bcc) structure, which is stable at high pressure or in confinement with stiff bcc metals like Nb. Molecular dynamics method is a magnificent tool to study material's structure and deformation mechanisms at the atomic level, however, requiring accurate interatomic potentials. The majority of most interatomic potentials available in the literature for Mg have only been fitted to the properties of its stable hcp phase. In the present work, we perform systematic study of applicability of currently available Mg potentials to modeling the properties of metastable bcc polymorph of Mg, taking into account cohesive energy curves, elastic constants, stacking fault energies, and phonon dispersion curves. We conclude that the modified embedded atom method (MEAM) potentials are the most suitable for investigating bcc Mg in Mg/Nb nano-composites, while the properties of high-pressure bcc Mg would be better modeled by neural network interatomic potentials after different local atomic environments corresponding to bcc Mg being included into the fitting database.

PbTe is a leading thermoelectric material at intermediate temperatures, largely thanks to its low lattice thermal conductivity. However, its efficiency is too low to compete with other forms of power generation. This efficiency can be effectively enhanced by designing nanostructures capable of scattering phonons over a wide range of length scales to reduce the lattice thermal conductivity. The presence of grain boundaries can reduce the thermal conductivity to ∼ 0.5 Wm −1 K −1 for small vacancy concentrations and grain sizes. However, grains anneal at finite temperature, and equilibrium and metastable grain size distributions determine the extent of the reduction in thermal conductivity. In the present work, we propose a phase-field model informed by molecular dynamics simulations to study the annealing process in PbTe and how it is affected by the presence of grain boundaries and voids. We find that the thermal conductivity of PbTe is reduced by up to 35% in the porous material at low temperatures. We observe that a phase transition at a finite density of voids governs the kinetics of impeding grain growth by Zener pinning.

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Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg.