Computational materials design and engineering C. J. Kuehmann* and G. B. Olson Computational materials design integrates targeted materials process–structure and structure– property models in systems frameworks to meet specific engineering needs. Design inherently consists of many competing requirements that require judicious decisions regarding key tradeoffs. The goal of computational materials design is to apply the best scientific understanding to facilitate decisions regarding the optimal tradeoffs that meet desired needs in the most time and resource efficient manner. Mechanistic materials design models require adequate fidelity to determine the favourability of one design solution over another but also the ability to be extrapolated over large parameter space to search for design optima in unexplored terrain. Design processes must not only efficiently find optimal solutions, but quickly identify failures. More broadly, materials design can only be as successful as the ability to identify the correct requirements for an application, and those requirements must address not only performance but also qualification hurdles including prediction of manufacturing variation. Keywords: Computational modelling, Materials design, Accelerated qualification Introduction The application of systems engineering principles, utilising computational materials science, allows the rapid and efficient development of design solutions for materials with specific application requirements. The general application of the technique has been well developed in other references. 1–4 Expansion of compu- tational materials design for the qualification and implementation of new materials into engineering systems was established by the US Defence Advanced Research Projects Agency (DARPA) Accelerated Insertion of Materials (AIM) programme. 5 Following up on these efforts, the US Office of Naval Research has partnered with DARPA and established the Digital 3-D (D-3D) initiative creating the next generation computational and analytical tools to support compu- tational materials engineering incorporating the AIM methodology. 6 The driver for an engineering approach to materials design is meeting a specific need for materials perfor- mance in the context of a system. Such a perspective necessitates the focus of the design activity on the wider context of the application. Combinations of properties must be considered within specified process, cost, environmental and life cycle constraints. Tradeoffs are inherent in the approach and tools to effect the design must address the optimisation of such tradeoffs expli- citly. An example of the systems approach is contained in Fig. 1 for ultrahigh strength corrosion resistant steel. The performance of the alloy is embodied in the combination of properties outlined in the column on the right. The design process determines suitable microstructural concepts to meet these property goals, as indicated by the middle column. Available processing paths to access the microstructural objectives are quantified in the left column. The links between the subsystem blocks in the flow block diagram represent process–structure and structure–property models required to quantitatively design an alloy to meet the desired performance objectives. The DARPA AIM programme extended the use of computational modelling into the challenges of scale up and materials qualification and certification. In the context of classical materials development, AIM applies to materials discovered experimentally in the laboratory and addresses the significant failure rate of such materials during scale-up and qualification. Applying advanced computational modelling to scale-up, AIM accelerates and reduces the risk associated with produ- cing new materials reliably at scale. In addition, the programme addressed the issue of establishing quanti- tative estimates of material property variation used for component design. Currently, such variation is quanti- fied by expensive and time consuming statistical experimentation. AIM applies a predictive probabilistic approach that integrates limited experimental data with computational materials modelling to efficiently estab- lish variability. In the pilot programme, AIM methods quantified the strength variation experienced in the production of 1000 turbine discs with as few as 15 individual measurements. 7 In the context of computa- tionally designed materials, AIM becomes even more effective since mechanistic modelling directly applicable QuesTek Innovations LLC, 1820 Ridge Ave., Evanston, IL, USA *Corresponding author, email ckuehmann@questek.com 472 ß 2009 Institute of Materials, Minerals and Mining Published by Maney on behalf of the Institute Received 12 February 2008; accepted 5 August 2008 DOI 10.1179/174328408X371967 Materials Science and Technology 2009 VOL 25 NO 4