A rules-driven, informatics-based approach to multiply-constrained materials design is outlined, employing the example of polymer coating design for silica fibers. This approach to the inverse mapping problem of structure generation from design constraints and quantitative structure-property relationships (QSPRs) emphasizes design rule generation and analysis. Using this approach addresses several issues in new materials discovery: 1) factoring a larger design problem into tractable components, 2) integrating physical and non-physical requirements (such as cost), 3) identifying information gaps that must be resolved to complete a design, and 4) identifying situations in which a solution consistent with known information is not feasible.

Fast and accurate calculation of the predicted results of Chemical Vapor Depositions (CVD) is very helpful to the high-throughput optimization of the CVD processes. In addition, robustness of the calculation process is important for automation of the optimization process. Therefore, we have developed a novel calculation method, by which robust and accurate calculations along with reduced computing cost were achieved, to reproduce deposition profiles in a macroscopic cavity (macrocavity). Boundary value problems for estimating diffusion-reaction equations by iterations of numerical integrations were changed into problems of finding the linear combinations consisted of a few functions. The coefficients of the linear combinations were optimized by Genetic Algorithms (GA). We could demonstrate the validity of the proposed method using various examples of the reaction mechanisms and conditions.

A combinatorial factory for the preparation and screening of polymeric coatings was developed. Coating formulations were prepared and coated using novel combinatorial techniques to obtain libraries of varying composition and thickness. The thickness of each film in a combinatorial array is rapidly determined via visible-light absorbance of optical dyes in conjunction with the Beer-Lambert relationship. These combinatorial libraries were then tested and screened using a variety of custom-made high-throughput methods. Combinatorial screening of oxygen and moisture vapor transmission rate, along with adhesive properties, are shown here. OTR and MVTR are determined using spectroscopic techniques. For adhesion, a spherical probe adhesive tester is able to generate parameters linked to tack, peel, and shear in one measurement. In addition to describing the testing methodology, benefits and shortcomings of these techniques will be highlighted.

A combination of quantum-chemical approaches including DFT, semiempirical PM3 and molecular mechanics (force field MM+) methods has been applied for analysis of the structure of polynuclear hydroxocomplexes of Zr in diluted solutions of its oxochloride as precursors of zirconia nanoparticles in zirconia-pillared clays. Relative stability of complexes differing by their size and shape has been estimated.

Combinatorial methodology for zeolite membrane development is discussed. Zeolite membrane is now attracting attentions as an energy-saving separation apparatus via pervaporation and/or vapor permeation, but sometimes it suffers performance (flux and/or separation factor) deterioration due to lack of chemical stability. We separate the development scheme into three steps – i) zeolite selection via chemical affinity and stability evaluation in powder applying combinatorial methodology, ii) membrane preparation for thus selected zeolite species, and iii) their evaluation in parallel manner. We have successfully developed hydrophilic zeolite membranes with tolerance against acidic environment. Thus developed zeolite membranes were successfully applied esterification reaction from a stoichiometric mixture of a carboxylic acid and an alcohol by assisting equilibrium shift via direct pervaporation from crude reaction solution.

The study of the structure and physical properties of atomic clusters is an extremely active area of research due to their importance, both in fundamental science and in applied technology. For medium size atomic clusters most of the structures reported today have been obtained by local optimizations of plausible structures using DFT (Density Functional Theory) methods and/or by global optimizations in which much more approximate methods are used to calculate the cluster’s energetics. Our previous work shows that these approaches can not be reliably used to study atomic cluster structures and that approaches based on global optimization schemes are needed. In this paper, we report the implementation and application of a parallel Genetic Algorithm (GA) to predict the structure of medium size atomic clusters.