Structural models provide an important source of hypothetical knowledge in scientific discovery. Informal Qualitative Models (IQMs) are structural models which can be applied to weak theory scientific domains. Example models are presented for the domain of solution chemistry. These models can be systematically generated, but, due to the weak theory nature of the domains to which they are applied, they cannot be verified directly. Instead, the application of IQMs to a problem can be used to drive other scientific discovery processes; in particular, the discovery of numeric laws. The HUM E system is a discovery system based around the application of IQMs. HUME's discovery goal is to construct explanations for phenomena, such as the depression of the freezing point of salt solutions, using a variety of reasoning strategies. HUM E first attempts to explain such phenomena using a pre-existing theory. If this theory is not able to provide an explanation, the system uses a combination of theory construction and numeric law discovery. The application of IQMs provides hypotheses for use by the other two processes. Used in this way, IQM application can be seen to provide a degree of explanatory support for numeric laws which would otherwise be simply descriptive generalisations of data. An example of the application of HUM E to a problem in solution chemistry is presented.

Much previous work in developing computational models of scientific discovery has concentrated on the formation of basic laws. The important role played by additional assumptions in this process is a neglected research topic. W e argue that hypotheses about structure are an important source of such additional assumptions, and that knowledge of this type can be embodied in the notion of Informal Qualitative Models (IQMs). In this paper, we demonstrate that such models can be synthesised by applying a set of operators to the most fundamental model in a domain. Heuristics are employed to control this process, which forms the basis of an architecture for model-driven scientific discovery. Conventional data-driven discovery techniques can be integrated into this architecture, resulting in laws which depend crucially on the model that is applied to a problem. This approach is illustrated by an historical survey of eighteenth and nineteenth century solution chemistry, which focuses on the evolution of the models employed by scientists. A series of models are synthesised which reflect these historical developments, showing the importance of structural models both in understanding certain aspects of the scientific discovery process, and as a basis for practical discovery systems.