A recursive-faulting model of distributed damage in confined brittle materials

We develop a model of distributed damage in brittle materials deforming in triaxial compression based on the explicit construction of special microstructures obtained by recursive faulting. The model aims to predict the effective or macroscopic behavior of the material from its elastic and fracture properties; and to predict the microstructures underlying the microscopic behavior. The model accounts for the elasticity of the matrix, fault nucleation and the cohesive and frictional behavior of the faults. We analyze the resulting quasistatic boundary value problem and determine the relaxation of the potential energy, which describes the macroscopic material behavior averaged over all possible fine-scale structures. Finally, we present numerical calculations of the dynamic multi-axial compression experiments on sintered aluminum nitride of Chen and Ravichandran [1994. Dynamic compressive behavior of ceramics under lateral confinement. J. Phys. IV 4, 177–182; 1996a. Static and dynamic compressive behavior of aluminum nitride under moderate confinement. J. Am. Soc. Ceramics 79(3), 579–584; 1996b. An experimental technique for imposing dynamic multiaxial compression with mechanical confinement. Exp. Mech. 36(2), 155–158; 2000. Failure mode transition in ceramics under dynamic multiaxial compression. Int. J. Fracture 101, 141–159]. The model correctly predicts the general trends regarding the observed damage patterns; and the brittle-to-ductile transition resulting under increasing confinement

A clustering algorithm to identify information subsystems

This paper presents an algorithm to cluster the entities and relationships identified by database designers into a set of internally cohesive subsystems. Our algorithm is based on the calculation of a distance score that is inversely related to the similarity of interactions of a pair of entities with the relationships in a binary entity-relationship matrix. Our algorithm avoids manual manipulation of rows and columns required by some of the available approaches (Feldman et al, 1986; Teorey, et al, 1989). It has been implemented on a PC, and does not require a super computer as the Wei and Gaither (1990) method does. Using a part-machine clustering problem presented by King (1980), we also show that our algorithm is superior to King\u27s rank order cluster algorithm which requires manual intervention to suppress exceptional entries before one can arrive at the final solution. Directions for further research are identified

Ranking the stars : a refined Pareto approach to computational materials design

We propose a procedure to rank the most interesting solutions from high-throughput materials design studies. Such a tool is becoming indispensable due to the growing size of computational screening studies and the large number of criteria involved in realistic materials design. As a proof of principle, the binary tungsten alloys are screened for both large-weight and high-impact materials, as well as for fusion reactor applications. Moreover, the concept is generally applicable to any design problem where multiple competing criteria have to be optimized

Transferable atomic multipole machine learning models for small organic molecules

Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation. The model is trained on quantum chemical results for atoms in varying chemical environments drawn from thousands of organic molecules. Multipoles in systems with neutral, cationic, and anionic molecular charge states are treated with individual models. The models' predictive accuracy and applicability are illustrated by evaluating intermolecular interaction energies of nearly 1,000 dimers and the cohesive energy of the benzene crystal.Comment: 11 pages, 6 figure