We have used a multi-objective genetic algorithm to optimize pseudopotentials for force accuracy and computational efficiency. Force accuracy is determined by comparing interatomic forces generated using the pseudopotentials and forces generated using the full-potential linearized augmented-plane wave method. This force-based optimization approach is motivated by applications where interatomic forces are important, including material interfaces, crystal defects, and molecular dynamics. Our method generates Pareto sets of optimized pseudopotentials containing various compromises between accuracy and efficiency. We have tested our method for LiF, Si0.5Ge0.5, and Mo and compared the performance of our pseudopotentials with pseudopotentials available from the ABINIT library. We show that the optimization can generate pseudopotentials with comparable accuracy (in terms of force matching and equation of state) to pseudopotentials in the literature while sometimes significantly improving computational efficiency. For example, we generated pseudopotentials for one system tested that reduced computational work by 71% without loss of accuracy. These results suggest our method can be used to generate pseudopotentials on demand that are tuned for a user’s specific application, affording gains in computational efficiency.

A variational principle is not generally satisfied in steady-state quantum transport as opposed to the case of ground-state problems. We show that for a short-range potential, a functional for the scattering amplitude can be introduced that is stationary for arbitrary variations about the exact scattering wave function. However, except for the special case of spherically symmetric potentials, the functional does not satisfy any minimum principle even in linear response and for single-channel scattering. The absence of a minimum principle puts severe limitations on the choice of trial wave functions in transport calculations. Examples of electronic transport in selected quantum wires will be presented to illustrate the problem.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1532 Advancing science through education in high performance computing Greg Walker and Alan Tackett Department of Mechanical Engineering Physics & Astronomy Department Vanderbilt University Abstract High-performance computing (HPC) platforms are becoming increasingly accessible to scientists and engineers due the remarkable decrease in commodity hardware costs. The promise of HPC allows engineers to perform more detailed analysis of complex systems in shorter times. Ultimately, design cycle times can be reduced and reliability can be increased by utilizing new HPC facilities. However, barriers to effective use of existing and emerging HPC technologies remain. In fact, few researchers and engineers possess the knowledge to benefit from the current computing capabilities. In response to this unheralded demand, a pilot course for exposing engineering students to new technologies and capabilities in the computing world has been developed. As a result, not only have student participants become HPC savvy, but also the research community as a whole has expressed intense interest in the continuation and expansion of the initial project. This surge in interest is derived from the fact that student participants have been able to solve problems that were previously not considered because of their computational requirements. In other words, science has been advanced because of this single class offering. Introduction Until recently, high-performance computing was the exclusive purview of highly special- ized research programs with large government grants.1 Further, the applications deemed worthy of such large-scale facilities and resources were usually defense related. As a re- sult, a cloud of mystery has surrounded scientific communities involved in development and implementation of both hardware and software devoted to solving computationally intense problems. Because of the tremendous expense of building and operating high-performance computing facilities, resources were scarce, and many researchers did not have the luxury of being able to consider scaling up their own projects. Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright c 2003, American Society for Engineering Education