Journal
COMPUTATIONAL MATERIALS SCIENCE
Volume 129, Issue -, Pages 311-322Publisher
ELSEVIER
DOI: 10.1016/j.commatsci.2016.11.041
Keywords
Optimal experimental design; Materials design; Mean objective cost of uncertainty (MOCU)
Categories
Funding
- Laboratory Directed Research and Development (LDRD) program at Los Alamos National Laboratory [20140013DR]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1422631, 1453563] Funding Source: National Science Foundation
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In this paper, we propose a general experimental design framework for optimally guiding new experiments or simulations in search of new materials with desired properties. The method uses the knowledge of previously completed experiments or simulations to recommend the next experiment which can effectively reduce the pertinent model uncertainty affecting the materials properties. To illustrate the utility of the proposed framework, we focus on a computational problem that utilizes time-dependent Ginzburg-Landau (TDGL) theory for shape memory alloys to calculate the stress-strain profiles for a particular dopant at a given concentration. Our objective is to design materials with the lowest energy dissipation at a specific temperature. The aim of experimental design is to suggest the best dopant and its concentration for the next TDGL simulation. Our experimental design utilizes the mean objective cost of uncertainty (MOCU), which is an objective-based uncertainty quantification scheme that measures uncertainty based upon the increased operational cost it induces. We analyze the performance of the proposed method and compare it with other experimental design approaches, namely random selection and pure exploitation. (C) 2016 Elsevier B.V. All rights reserved.
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