Predicting the Volumes of Crystals

New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis, or as the initial guess for structural optimization in electronic structure calculations, both of which usually require a reasonable guess of the lattice parameters. In this work, we propose two lattice prediction schemes to improve the initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of species in the candidate crystal structure to a known crystal structure, while the second scheme relies on data-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structure and does not require a reference structure. We demonstrate that the two schemes can effectively predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. We also discuss the various factors that may impact the performance of the schemes. Implementations for both schemes are available in the open-source pymatgen software.Comment: 8 figures, 2 table

Barrettes Designed as Friction Foundations: A Case History

This paper describes the performance of a fully instrumented test barrette subjected to an ultimate loading of 30932 kN. The load transfer characteristics were measured by vibrating wire strain gauges. Rod extensometers recorded the displacements at several locations along the barrette shaft. Test results indicated that substantial loads were carried in shaft resistance. The end bearing component was reduced by poor toe conditions caused by debris accumulation at the trench base. The load - displacement behaviour and factor of safety in barrette foundation design LS discussed. The toad test results of a subsequent working barrette confirmed that the performance of the barrettes designed as friction foundations in the Old Alluvium is satisfactory

Accurate Force Field for Molybdenum by Machine Learning Large Materials Data

In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods still do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including energies, forces, stresses, elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.Comment: 25 pages, 9 figure