High-Throughput Experimental Measurements and Holistic Integration with Computational Data for Computational Thermodynamics and Kinetics

Experimental techniques for rapid collections of materials data and holistic approaches to integrate experimental and computational data will be described with examples. Localized property measurements on composition gradients created in diffusion multiples allow high-throughput collection of several materials properties as a function of composition, in addition to phase diagrams and diffusion coefficients. Experimental phase diagram data are essential for reliable assessments of thermodynamic parameters for computational design of alloys. An approach was demonstrated to establish reliable diffusion coefficient (atomic mobility) databases by holistically integrating both experimental and computational data. This approach together with much simplified models for diffusion coefficient will enable more reliable diffusion databases to be established rapidly for various simulations of materials processes. An approach that iteratively and holistically integrate experimental results with model predictions can be very effective in both establishing materials databases and accelerating alloy design. Additive manufacturing (AM, 3D printing) also provides a great opportunity to further expand high-throughput experimentation and accelerated materials discovery.