Seeking structure-property relationships is an accepted paradigm in materials science, yet these relationships are often not linear. The challenge is to be able to link materials behavior and seek patterns among multiple length and time scales. Materials informatics and statistical learning techniques permit one to survey complex, multiscale information in an accelerated and statistically robust and yet meaningful manner. When this is coupled with advanced tools for computational thermodynamics and kinetic simulations, the result is a powerful computational infrastructure for materials design. This paper provides an overview of the value of this integrated approach to materials modeling in addressing the challenges in linking length scales.
All Science Journal Classification (ASJC) codes
- Materials Science(all)