Predictive science and engineering of materials & devices: towards cyber & computationally enabled decision-making

By Alejandro Strachan

Purdue University



Published on


The synergistic integration of predictive, physics-based modeling and experiments within a decision-making framework has the potential to revolutionize design and certification of materials and devices, reducing the cost and time involved in discovery and deployment. The impact of this approach is expected be particularly high in emerging applications where significant breakthroughs are needed to transform laboratory demonstrations and concepts into products that benefit society. Achieving this goal requires the rapid transition of cutting-edge research codes from developers to researchers and instructors who can use them in design and optimization and to train next generation of engineers and scientists. In addition, decision-making requires quantified confidence in the predictions via an optimal combination of simulations and experiments accounting for uncertainties and variability of disparate origins.

To illustrate recent progress and remaining challenges in this presentation I will describe work in the Center for the Prediction of Reliability, Integrity and Survivability of Microsystems (PRISM). PRISM's goal is to predict the performance of a RF-MEMS switch from electrons and atoms with quantified uncertainties. To achieve this goal we integrate multiscale modeling and experimentation using a Bayesian approach to account for variabilities and uncertainties at multiple scales and rigorously quantify the confidence in the predictions. The resulting predictions with quantified uncertainties provide the type of probabilities information needed for decision-making. I will also discuss ongoing work to make uncertainty quantification tools available in nanoHUB to serve the nanotechnology & materials community; the infrastructure is generally applicable and we foresee these efforts will be adopted by other HUBzero communities.


Alejandro Strachan is a Professor of Materials Engineering at Purdue University and the Deputy Director of NNSA's Center for the Prediction of Reliability, Integrity and Survivability of Microsystems. Before joining Purdue, he was a Staff Member in the Theoretical Division of Los Alamos National Laboratory and worked as a Postdoctoral Scholar and Scientist at Caltech. He received a Ph.D. in Physics from the University of Buenos Aires, Argentina, in 1999. Among other recognitions, Prof. Strachan was named a Purdue University Faculty Scholar (2012-2017), received the Early Career Faculty Fellow Award from TMS in 2009 and the Schuhmann Best Undergraduate Teacher Award from the School of Materials Engineering, Purdue University, in 2007.

Prof. Strachan's research focuses on the development of predictive atomistic and molecular simulation methodologies to describe materials from first principles, their application to problems of technological importance and quantification of associated uncertainties. Application areas of interest include: coupled electronic, thermal and mechanical processes in nano-electronics, MEMS and energy conversion devices; thermo-mechanical response and chemistry of polymer composites and molecular solids as well as active materials including shape memory and high-energy density materials.

Cite this work

Researchers should cite this work as follows:

  • Alejandro Strachan (2013), "Predictive science and engineering of materials & devices: towards cyber & computationally enabled decision-making,"

    BibTex | EndNote


Nikki Huang

Purdue University


  • Copyright © 2022 Hubzero
  • Powered by Hubzero®