USACM Nanotech webinar, Wed, Aug 30, 2:00pm-3:00pm CDT: by Krishna Garikipati. A free energy-based framework for scale bridging in crystalline solids--with some use of machine learning methods

Upcoming USACM Nanotechnology webinar on Wednesday, Aug 30, 2:00 pm - 3:00 pm CDT.

Join Zoom Meeting

https://us06web.zoom.us/j/84541274013?pwd=ZDhuM09BaUlVVlBuREhEV0sweGFlZz09

Meeting ID: 845 4127 4013                Passcode: 799065

USACM Nanotechnology Virtual Seminar

A free energy-based framework for scale bridging in crystalline solids--with some use of machine learning methods

Speaker

Krishna Garikipati, University of Michigan

Discussants

Harley T. Johnson, University of Illinois Urbana-Champaign

Shailendra P. Joshi, University of Houston

Abstract

The free energy plays a fundamental role in theories of phase transformations and microstructural evolution in crystalline solids. It encodes the thermodynamic coupling between mechanics and chemistry within continuum descriptions of non-equilibrium materials phenomena. In mechano-chemically interacting materials systems, consideration of compositions, order parameters and strains results in a high-dimensional free energy density function. Since its origins lie in the electronic structure, a rigorous representation of the free energy presents a framework for scale bridging in solids. In this study we have been exploring such a framework, while developing practical machine learning methods to contend with high dimensionality and efficient sampling. We have developed integrable deep neural networks (IDNNs) that are trained to free energy derivative data generated by statistical mechanics simulations. The latter are based on cluster Hamiltonians, themselves trained on density functional theory calculations.  The IDNNs can be analytically integrated to recover a free energy density function. We combine the IDNNs with active learning workflows for well-distributed sampling of the free energy derivative data in high-dimensional input spaces. This enables scale bridging between first-principles statistical mechanics and continuum phase field models. As prototypical material systems we focus on applications in Ni-Al alloys and in the battery cathode material: Li$_x$ CoO$_2$.

Bio: Krishna Garikipati obtained his Ph.D. at Stanford University in 1996, and after a few years of post-doctoral work, he joined the University of Michigan in 2000, where is now a Professor in the Departments of Mechanical Engineering and Mathematics. Between 2016 and 2022, he served as the Director of the Michigan Institute for Computational Discovery & Engineering (MICDE). His research is in computational science, with applications drawn from biophysics, materials physics, mechanics, and mathematical biology. Of recent interest are data-driven approaches to computational science. He has been awarded the DOE Early Career Award for Scientists and Engineers, the Presidential Early Career Award for Scientists and Engineers (PECASE), and a Humboldt Research Fellowship. He is a fellow of the US Association for Computational Mechanics, and the International Association for Computational Mechanics, a Life Member of Clare Hall at University of Cambridge, and a visiting scholar in Computational Biology at the Flatiron Institute of the Simons Foundation.