Most scientists conduct analyses and run models in several different software and hardware environments, mentally coordinating the export and import of data from one environment to another. The Kepler scientific workflow system provides domain scientists with an easy-to-use yet powerful system for capturing scientific workflows (SWFs). SWFs are a formalization of the ad-hoc process that a scientist may go through to get from raw data to publishable results. Kepler attempts to streamline the workflow creation and execution process so that scientists can design, execute, monitor, re-run, and communicate analytical procedures repeatedly with minimal effort. Kepler is unique in that it seamlessly combines high-level workflow design with execution and runtime interaction, access to local and remote data, and local and remote service invocation. SWFs are superficially similar to business process workflows but have several challenges not present in the business workflow scenario. For example, they often operate on large, complex and heterogeneous data, can be computationally intensive and produce complex derived data products that may be archived for use in reparameterized runs or other workflows. Moreover, unlike business workflows, SWFs are often dataflow-oriented as witnessed by a number of recent academic systems (e.g., DiscoveryNet, Taverna and Triana) and commercial systems (Scitegic/Pipeline-Pilot, Inforsense). In a sense, SWFs are often closer to signal-processing and data streaming applications than they are to control-oriented business workflow applications.

High performance computing, storage, visualization, and database infrastructures are increasing geometrically in complexity as scientists move towards grid-based computing. While this is natural, it has the effect of pushing computational capabilities beyond the reach of scientists because of the time needed to harness the infrastructure. Hiding the complexity of networked resources becomes essential if scientists are to utilize them effectively. In this work, we describe our efforts to integrate various computational chemistry components into a scientific computing environment. We briefly describe improvements we have made to individual components of the chemistry environment as well as future directions, followed by a more in-depth discussion of our strategy for integration into a grid workflow environment based on web services, which enables access to remote resources while shielding users from the complexities of the grid infrastructures. A preliminary schema for storing data obtained from computational chemistry calculations is also described.