Interatomic potentials for atomistic simulations of the Ti-Al system

Semi-empirical interatomic potentials have been developed for Al, alpha-Ti, and gamma-TiAl within the embedded atomic method (EAM) by fitting to a large database of experimental as well as ab-initio data. The ab-initio calculations were performed by the linear augmented plane wave (LAPW) method within the density functional theory to obtain the equations of state for a number of crystal structures of the Ti-Al system. Some of the calculated LAPW energies were used for fitting the potentials while others for examining their quality. The potentials correctly predict the equilibrium crystal structures of the phases and accurately reproduce their basic lattice properties. The potentials are applied to calculate the energies of point defects, surfaces, planar faults in the equilibrium structures. Unlike earlier EAM potentials for the Ti-Al system, the proposed potentials provide reasonable description of the lattice thermal expansion, demonstrating their usefulness in the molecular dynamics or Monte Carlo studies at high temperatures. The energy along the tetragonal deformation path (Bain transformation) in gamma-TiAl calculated with the EAM potential is in a fairly good agreement with LAPW calculations. Equilibrium point defect concentrations in gamma-TiAl are studied using the EAM potential. It is found that antisite defects strongly dominate over vacancies at all compositions around stoichiometry, indicating that gamm-TiAl is an antisite disorder compound in agreement with experimental data.Comment: 46 pages, 6 figures (Physical Review B, in press

Mining Data From a Knowledge Management Perspective: an application to outcome prediction in patients with resectable hepatocellular carcinoma

This paper presents the use of data mining tools to derive a prognostic model of the outcome of resectable hepatocellular carcinoma. The main goal of the study was to summarize the experience gained over more than 20 years by a surgical team. To this end, two decision trees have been induced from data: a model M1 that contains a full set of prognostic rules derived from the data on the basis of the 20 available factors, and a model M2 that considers only the two most relevant factors. M1 will be used to explicit the knowledge embedded in the data (externalization), while the model M2 will be used to extract operational rules (socialization). The models performance has been compared with the one of a Naive Bayes classifier and have been validated by the expert physicians