The quantitativelly accurate prediction of materials behavior from first principles requires the characterization of a wide range of phenomena with disparate temporal and spatial scales from electrons and atoms to devices. No single theory of computational model can capture all these phenomena with the required level of accuracy; thus, a multi-scale, multi-physics approach involving a combination of theories and computational techniques is necessary. This tutorial will describe some of the most powerful and widely used techniques for materials modeling including i) first principles quantum mechanics (QM), ii) large-scale molecular dynamics (MD) simulations and iii) mesoscale modeling together with the strategies to bridge between them. I will also exemplify the use of these computational techniques to characterize mechanical, chemical, and structural properties of a variety of materials, from metals to energetic materials. Based on first principles, the techniques and strategy described here are predictive and should be useful to help guide the design and optimization of new materials or devices.
Alejandro Strachan is an Assistant Professor of Materials Engineering at Purdue University. He got his doctoral degree in Physics from the University of Buenos Aires, Argentina. Before joining Purdue, Prof. Strachan was a staff member at Los Alamos National Laboratory and worked at the California Institute of Technology. Prof. Strachan's research focuses on developing and validating computational methodologies aimed at predicting the behavior of materials from first principles and their application in technologically relevant areas where a molecular-level understanding is lacking and can help solve outstanding problems. Areas of interest include: active and energetic materials, mechanical properties of nanoscale or nano-structured materials, and computational materials design.
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- general tools
- multiscale models
- nano electro-mechanical systems
- materials science