Materials design has been accelerated under the Materials-Genome Initiative by simulating materials with many possible input parameters (composition, processing, etc.) in order to predict those combinations that will yield desired properties, for example, more efficient thermoelectrics for cooling applications. In any such design cycle, there are uncertainties in the inputs, and it is important to understand how these uncertainties propagate to uncertainties in the output properties. For complex computations, uncertainty propagation has traditionally been estimated with random ("Monte-Carlo") sampling, but when each calculation takes substantial computer time, such sampling becomes prohibitively expensive. A major outcome of this project is the development and public release of efficient software to propagate uncertainty across computational materials-science calculations. While the focus will be on simulations of the evolution of materials microstructures, the proposed framework will be applicable to a wide range of materials-science-relevant modeling tools. Activities will also be conducted to increase the impact of the project. These include the generation of an extensive library of 2D and 3D microstructures that can be used as model systems to teach and develop new frameworks for microstructure informatics, the creation of a microstructure-zoo citizen-science platform to assist in labeling and annotating synthetic microstructures created in this project, and the creation of interdisciplinary training tools for uncertainty quantification in computational materials science. This award will also support the training of two graduate students at the interface of materials science and statistical analysis.
The objective of this research is to create sequentially optimal sampling policies for the propagation of uncertainty from model inputs to model outputs. The problem considered here is that of conducting accurate and efficient uncertainty propagation when faced with computationally expensive models, specifically phase-field models of microstructure evolution in thermoelectrics and other materials. Expensive in this research refers to models for which resources will only permit order 10 model evaluations prior to the need to make a decision informed by the uncertainty analysis. Such models are prevalent in many fields, though the focus here will be on computational materials-science applications. Specifically, in this project, the research team will demonstrate the efficient propagation of uncertainty across CALPHAD-Phase Field Model chains that attempt to describe the microstructure evolution of materials under chemical and elastic driving forces. These models tend to be highly non-regular in that the model output can differ qualitatively depending on the region in the input/parameter space being sampled. Moreover, they are computationally expensive, with full three-dimensional realizations of the simulations requiring upwards of 10,000 CPU-hours. In addition, the input/parameter space is often high dimensional, with more than 20 stochastic input conditions and model parameters. One of the main features of this uncertainty propagation framework is that it is non-intrusive, as it works on the input space of the models. Thus, the only information needed is the joint probability distribution of the input parameters, which makes the framework widely applicable, beyond the specific test problem(s) used to develop it. The framework is capable of reweighting previously executed model evaluations in order to maximize efficiency for uncertainty propagation with negligible additional computational expense. The PIs will release uncertainty-propagation and phase-field code through a Github repository under open licenses. They will set up a public Materials Models and Data Management System (MMDMS) to make the phase-field and DFT data they generate in the course of this research widely available and coordinate with other data repositories.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
