SES 2023 : Symposium 5.2 - Advances in Multiscale Modeling and Machine Learning in Nanomechanics
ABSTRACT SUBMISSION (Click Here)
Description:
Nanomaterials have enormous applications in various fields, such as energy storage, electronics, optical devices, sensors, and nanoelectromechanical devices. Efficient and robust computational techniques are essential in characterizing and designing novel nanomaterials/structures with unique and extraordinary properties tailored to technological applications. This symposium solicits research presentations describing novel approaches as well as methodological challenges in modeling (atomistic, molecular, continuum) and machine learning techniques that have implications in nanomechanics. The emphasis is on the highly interdisciplinary research work at the intersection of mechanics, applied mathematics, physics, chemistry, materials science, and high-performance computing. The symposium topics include (but are not limited to) the following:
**** Multiscale modeling of nanomaterials; Modeling epitaxial growth of low-dimensional materials, Nanomechanics of energy storage materials (batteries).
**** Data analytics for designing materials for various applications such as batteries, photovoltaics, fuel cells, thermoelectrics, etc; Metaheuristic optimization of material compositions and atomic structures.
**** Recent advances in atomistic/molecular modeling methods, atomistic-informed continuum modeling.
**** Recent advances in machine learning for materials discovery.
Organizers:
Steven W. Cranford, Editor-in-Chief, Cell Press Matter, USA
Phanish Suryanarayana, Georgia Institute of Technology, USA
Dibakar Datta, New Jersey Institute of Technology (NJIT), USA