A Bronze Age Round Barrow Cemetery, Pit Alignments, Iron Age Burials, Iron Age Copper Working, and Later Activity at Four Crosses, Llandysilio, Powys.

Excavation undertaken at the Upper Severn valley round barrow cemetery at Four Crosses, Llandysilio between 2004 and 2006 has increased the known barrows and ring-ditches to some 26 monuments, and revealed additional burials. Based on limited dating evidence, and the data from earlier excavations, the majority of the barrows are thought to be constructed in the Bronze Age. The barrows are part of a larger linear cemetery and the landscape setting and wider significance of this linear barrow cemetery are explored within this report. Dating suggests two barrows were later, Iron Age additions. The excavation also investigated Iron Age and undated pit alignments, Middle Iron Age copper working and a small Romano-British inhumation cemetery and field systems. Much of this evidence reflects the continuing importance of the site for ritual and funerary activity

TBMaLT, a flexible toolkit for combining tight-binding and machine learning

Tight-binding approaches, especially the Density Functional Tight-Binding (DFTB) and the extended tight-binding schemes, allow for efficient quantum mechanical simulations of large systems and long-time scales. They are derived from ab initio density functional theory using pragmatic approximations and some empirical terms, ensuring a fine balance between speed and accuracy. Their accuracy can be improved by tuning the empirical parameters using machine learning techniques, especially when information about the local environment of the atoms is incorporated. As the significant quantum mechanical contributions are still provided by the tight-binding models, and only short-ranged corrections are fitted, the learning procedure is typically shorter and more transferable as it were with predicting the quantum mechanical properties directly with machine learning without an underlying physically motivated model. As a further advantage, derived quantum mechanical quantities can be calculated based on the tight-binding model without the need for additional learning. We have developed the open-source framework—Tight-Binding Machine Learning Toolkit—which allows the easy implementation of such combined approaches. The toolkit currently contains layers for the DFTB method and an interface to the GFN1-xTB Hamiltonian, but due to its modular structure and its well-defined interfaces, additional atom-based schemes can be implemented easily. We are discussing the general structure of the framework, some essential implementation details, and several proof-of-concept applications demonstrating the perspectives of the combined methods and the functionality of the toolkit

Life by the river: a prehistoric landscape at Grendon, Northamptonshire

This paper describes the development of a prehistoric landscape by the river Nene at Grendon Lakes, partly revealed in the 1970s and partly during excavations in 1998 and 2001, which are reported in full. Two major phases of archaeological activity are evident, one interpreted as Neolithic�Early Bronze Age, the other as Iron Age. The gap between these is bridged by an environmental sequence reconstructed with the aid of a pollen core from an adjacent palaeochannel, which shows that human activity continued in the intervening period. The landscape is comparable in form, though not in scale, with that investigated 13 km downstream at Raunds, and helps shed light on the distinctive features of Midlands river valleys like the Nene in prehistory. In conclusion it is suggested that the different characters of the Neolithic and Iron Age features at Grendon mask some underlying similarities in the way they structured people�s movements and encounters

Structure and ion transport of lithium-rich Li1+xAlxTi2−x(PO4)3 with 0.3

© 2020 Elsevier B.V. New solid state electrolytes are becoming increasingly sought after in the drive to replace flammable liquid electrolytes. To this end, several Li conducting solids have been identified as promising candidates including Li stuffed garnets and more recently Li-rich materials such as Li1+xAlxTi2−x(PO4)3 with 0.3Ti4+ compared to Li+–>Al3+. Furthermore, our calculated ionic conductivities are in excellent agreement with experimental values, highlighting the robustness of our computational models