Abstracts Book - IMRC 2018

• SD2-O032 Invited Talk
LEVERAGING MACHINE LEARNING AND FIRST PRINCIPLES
MODELING TO ACCELERATE MATERIALS CHARACTERIZATION
AND DESIGN
Maria Chan 1 , Arun Mannodi Kanakkithodi 1 , Spencer Hills 1 , Eric Schwenker 1,2 , Fatih Sen 1 , Liang
Li 1 , Kendra Letchworth Weaver 1
1 Argonne National Laboratory, Center for Nanoscale Materials, United States. 2 Northwestern
University, , United States.
Improvements in renewable energy technologies, such as in photovoltaic or
catalytic energy conversion, or energy storage, require significant efforts in
characterization of existing materials and design of new ones. Advances in firstprinciples
theories, algorithms and computational capabilities have enabled
efficient and accurate calculations of materials properties first principles.
However, first principles modeling requires as inputs atomistic configurations,
and the determination of atomistic structures in systems with reduced
symmetry, such as nanostructures and defects, is particularly challenging due
to the large number of possible configurations. In this talk, we will discuss how
physics-informed machine learning is used to combine first principles modeling
and materials characterization data, such as x-ray and electron microscopies, to
allow for a high-confidence solution of atomistic structure. In addition, we will
discuss efforts in improving computational predictions of properties and
characterization data, machine learning approaches to develop mapping
between structures, characterization signatures, and functional properties.
Keywords: Density functional theory, Machine learning, Renewable Energy Materials
Presenting authors email: mchan@anl.gov