Recently, there have been a number of reports identifying technologies of strategic importance. These technologies, which reflect the full range of national critical technology needs, are: materials, manufacturing, informatics and computing, biotechnology/life sciences, aeronautics/surface transport and energy/environment. In support of these technologies there has been much discussion on their research infrastructure, e.g., instrumentation, telecommunication networks, and supercomputing facilities. With the exception of biotechnology/life sciences, however, there has been little discussion on the uses of evaluated numeric and factual databases at the research level. The uses of databases are more advanced in biotechnology and life science research than in other fields, and this has been driven by the needs of genetic research, protein engineering, and drug design, where researchers need data and models for the design of new products. The needs of databases and their manipulation tools in materials science research are also essential in developing an intelligent research infrastructure. Given the present financial constraints, there is a need to use existing funds more efficiently and effectively. One way to achieve this is to use all available experimental data from various intersecting disciplines and bind them together with knowledge which will aid in the design of new materials.

The prediction of new materials is, in theory, a completely straightforward problem. The fundamental equations of quantum mechanics for solids are well-established and can be numerically solved, at least approximately, by powerful first-principles methods developed over the last decade. It remains, then, only to examine the solutions to find those systems that will exhibit the properties desired.

The number of possibilities to be considered and sorted is many orders of magnitude larger than can be managed in a case-by-case first-principles analysis. Therefore, in practice, the approach to theoretical prediction of new materials is to establish easy-to-apply rules for screening large numbers of candidate stoichiometrics (which might or might not be known compounds). These rules are based on physical understanding of the occurrence of the structure or property, inference from known examples, and general principles governing chemical trends in the structure and stability of known compounds. Data from the scientific literature on the occurrence and structure of crystalline compounds are now organized into a number of crystallographic databases, providing a unique opportunity to investigate, from a global perspective, the factors that influence structure and stability. To display, access, and extract general principles from this enormous amount of information, a graphical method such as the “quantum diagram” method is essential, and can be thought of as providing a convenient “roadmap” for navigation around the database. Useful conclusions about individual compounds or small sets of compounds can also be drawn from this type of analysis. These conclusions form the basis for rules for predicting new representatives that direct attention to special regions on the roadmap, as illustrated on the cover of this issue. Systems that satisfy these rules are the most likely to reward experimental investigation.

Some twenty years ago, it appeared that the crystallographic database had grown so large that it could no longer be described by conventional methods (see, for example, Structure Reports). Today we have available both computerized databases (CRYSTDAT, JCPDS diffraction data, etc.) and excellent bibliographies which enable us to search much larger databases rapidly and far more systematically, resulting in qualitative changes in methods and results. Following are five examples illustrating this point.

There have been many achievements in the last decade in the development of materials science, chemistry, and physics (experimental and theoretical). However, the most difficult problem—calculating the intrinsic properties of multicomponent compounds starting from the knowledge of their constituent components' properties—still remains unsolved. Calculations or predictions based on only the properties of constituent components (or simply, properties) are called a priori calculations or predictions. These difficulties are due to the solution of mathematical problems arising in the quantum mechanical calculations of multi-electronic systems. As a result, scientists make use of many empirical prediction methods that use existing regularities from a variety of property data. Some of the empirical criteria for the formation of compounds with predefined properties use the rules of Hume-Rothery, Laves, Mathias, Goldschmidt, Villars, and Darken-Gurry.

History is replete with examples of the enormous impact of data compilations in all fields of science and technology. The advantages and opportunities such aggregations confer include:

• convenience of access,

• condensation and homogenization of raw data,

• formats tailored for the application,

• perception of patterns,

• detection of errors,

• definition of gaps and inconsistencies, and

• basis for formulating and testing theories.

The opportunities in materials science, and specifically in the subfield of alloy design, are no fewer than in other fields of science. Indeed, as I have remarked in a review of the history of the development of understanding and application of intermetallic compounds, breakthroughs in this field have usually come about not from “a new technique, a unique experimental observation, or an abstruse theory… Rather it was the amassing of a critical volume of experimental data in the literature which permitted an individual with great insight to perceive an underlying pattern not previously apparent.”

Since 1987, an intensive Portuguese-Spanish collaboration program has been developed on microstructural characterization of LiNbO3 by nuclear (ion-beam and hyperfine interaction) techniques. The collaboration involves the Department of Applied Physics of the Universidad Autónoma de Madrid, on the Spanish side, and the Center of Nuclear Physics and the Department of Physics of LNETI, on the Portuguese side. The combined effort of the two latter institutions is now channeled through the recently created Center for Materials Characterization and Processing.

LiNbO3 is an important material for optoelectronic applications, including light modulation, microwave filters, frequency doublers, and those applications based on the photorefractive effect. Photorefractive applications, still at the laboratory level, include holographic information storage, coherent beam amplification, beam deflectors, and applications relying on the production of phase-conjugated beams by four-wave mixing. LiNb03 is particularly useful for optical information processing, optical interconnects, associative memories, and neural net implementations. These applications involve bulk LiNbO3, but mostly waveguide structures fabricated in LiNbO3 substrates. Recently the development of efficient waveguide lasers on ion-implanted and proton-exchanged waveguides has opened a new field whose final objective is the fabrication of integrated laser modulators and frequency doublers on the same “chip.” The mastering and optimization of all these applications rely on an adequate microstructural characterization of the material with particular reference to both intrinsic and extrinsic point defects. The knowledge and control of this microstructure should permit the fabrication of well-characterized and reproducible devices.