Materials science is of fundamental significance to science and technology because our industrial base and society depend upon our ability to develop advanced materials. Materials and materials processing cuts across almost every sector of industry. The key in all of these areas is the ability to rapidly screen possible designs which will have significant impact. However up to now materials design and processing have been to a large extent empirical sciences. In addition we are still unable to design new alloys and polymers to meet application specific requirements. Being able to do so quickly and at minimum cost would provide an incredible advantage. Obviously, the ability to predict physical, chemical, or mechanical properties of compounds prior to their synthesis is of great technological value in optimizing their design, processing, or recycling. In addition, in order to realize the ultimate goal of materials by computational design, the reverse problem, prediction of chemical structure based on desired properties, has to be resolved. Research at ORNL has lead to the development of a novel computational paradigm (coupling computational neural networks with graph theory, genetic algorithms, wavelet theory, fuzzy logic, molecular dynamics, and quantum chemistry) capable of performing accurate computational synthesis (both predictions of properties or the design of compounds that have specified performance criteria). The computational paradigm represents a hybrid of a number of emerging technologies and has proven to work very well for test compounds ranging from small organic molecules to polymeric materials. Fundamental to the method is the neural network-based formulation of the correlations between structure and properties. The advantages of this method is in its ease of use, speed, accuracy, and that it can be used to predict both properties from structure, and also structure from properties
