pathways for chemical reactions, and the properties and dynamics of large molecules or large assemblies of molecules. All of these activities have been extraordinarily successful over the past decades and have firmly established computational chemistry as a third methodology alongside experiment and theory. Computational chemists have also been among the foremost users of computer hardware, with substantial requirements for computer time, memory, and disk space, and with a history of exploiting both early access to new architectures and novel solutions to reduce the cost of performing their research. They have successfully exploited new vector and parallel computer architectures as they have become available, and at the same time have developed new algorithms to efficiently use first minicomputers and later RISC workstations, and most recently clusters of commodity PCs. The advent of ASCI-class computing resources presents a new opportunity—a vast increase in computational capability to be exploited. However, it is not obvious that the community is ready to use such massively parallel machines, not the least because even our scalable algorithms have generally been tested in situations only up to a few hundred processors. To use the full power of an ASCI-class machine will require successfully harnessing at least an order of magnitude more processors. Will our current algorithms, and at an even higher level, the computational chemistry methods they implement, be suitable for such architectures? It is these questions we concentrate on here, not the detail issues of whether message-passing implementations are more or less appropriate than shared-memory implementations of our methods. We demonstrate, for example, that much of our current methodology is incapable of extending the accuracy of our description of molecular systems beyond what we can currently achieve, and that new methods must be sought.
We take quantum chemistry, as defined above, as an example. The information for nonempirical dynamics calculations comes from quantum-chemical calculations, so in this sense quantum chemistry is fundamental to nonempirical computational chemistry. Typical quantum chemistry calculations, in 1999, treat a single molecule or perhaps a group of molecules, in vacuo, at a temperature of 0 K. The accuracy achievable varies with the size of the molecule (see Figure 2.1), but the highest-accuracy work is comparable to experimental accuracy for many properties. A simple way to represent the relationship between molecular size and accuracy of results is a graph generally referred to as a Pople diagram.