Predictive Theory of Materials
Study quantum mechanics and molecular modeling to understand material properties and behavior at the atomic level.
Study quantum mechanics and molecular modeling to understand material properties and behavior at the atomic level.
This advanced course provides a unified framework for understanding materials at atomic scales. Starting with quantum mechanics fundamentals, students explore electronic structures of atoms, chemical bonding, and crystal structures. The curriculum progresses through molecular dynamics, statistical mechanics, and practical applications using nanoHUB simulations. Students gain hands-on experience with density functional theory and molecular dynamics to predict material properties and processes.
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What you'll learn
Master quantum mechanics principles and their applications to materials
Understand statistical mechanics connections between atomic and macroscopic scales
Use density functional theory for materials property prediction
Apply molecular dynamics to simulate atomic processes
Analyze electronic structure and bonding in molecules and crystals
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This comprehensive course explores the fundamental physics governing materials at atomic scales. Students learn to bridge quantum mechanics principles with macroscopic material properties through theoretical frameworks and computational simulations. The curriculum combines core concepts in quantum mechanics, molecular dynamics, and statistical mechanics with practical applications using nanoHUB simulation tools.
Quantum Mechanics and Electronic Structure
Module 1
Electronic Structure and Bonding of Molecules and Crystals
Module 2
Dynamics of Atoms: Classical Mechanics and MD Simulations
Module 3
Connecting Atomic Processes to the Macroscopic World
Module 4
Advanced Topics and Case Studies
Module 5
Fee Structure
Instructors
Leading Materials Science Innovator and Computational Expert
Alejandro Strachan serves as a Reilly Professor of Materials Engineering at Purdue University, where he leads groundbreaking research in predictive atomistic and molecular simulation methodologies. After earning his Ph.D. in Physics from the University of Buenos Aires in 1998, he gained valuable experience as a Staff Member at Los Alamos National Laboratory and as a Postdoctoral Scholar and Scientist at Caltech before joining Purdue. His research portfolio spans the development of computational techniques for materials science, focusing on coupled electronic, chemical, and thermo-mechanical processes in nanoelectronics, polymers, molecular solids, and active materials. With over 200 peer-reviewed publications, his scientific impact has earned him numerous accolades, including recognition as a Purdue University Faculty Scholar (2012-2017), the TMS Early Career Faculty Fellow Award (2009), and an R&D 100 award for nanoHUB software services
David Guzman is a Ph.D. candidate in Materials Engineering at Purdue University, working under the guidance of Professor Alejandro Strachan. His research specializes in ab initio simulations of electronic processes in dielectric materials, particularly focusing on electrochemical resistance drift applications. His expertise extends to the characterization of two-dimensional materials for electronic and energy harvesting applications. As part of his doctoral work, Guzman contributes to two signif
Sam Reeve is a Ph.D. student in the Strachan Research Group within the School of Materials Engineering at Purdue University, building upon his B.S. in Materials Engineering from Iowa State University (2013). His research focuses on the intersection of materials and computational sciences, with particular emphasis on atomistic simulations and uncertainty quantification. His work spans investigating novel properties of martensitic and ferroelectric nanostructures and understanding errors in molecular dynamics input models. As a Teaching Assistant at Purdue University, his excellence in education has been recognized with two Magoon Awards for outstanding undergraduate teaching from the Purdue University College of Engineering. His contributions to the field include the development of multiple simulation tools for materials research and education, demonstrating his commitment to both advancing scientific knowledge and improving educational resources in materials science.
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