Abstracts Book - IMRC 2018

• SD2-O023
META-STABLE PHASE DIAGRAM PREDICTION USING MACHINE
GUIDED FREE ENERGY OPTIMIZATION.
Troy Loeffler 1 , Srilok Srinivasan 1 , Subramanian Sankaranarayanan 1
1 Argonne National Laboratory, Center for Nanoscale Materials, United States.
Despite their importance in the role of crystalization, the systematic prediction
and quantification of meta-stable crystal phase is an important yet diffucult task
in the field of computational material science. Due to the overwhelming
number of potential configurations any given system may take, it is extremely
difficult to find new structures without explicit a priori knowledge of what the
structure may look like. In addition to this, the structure may only be stable for
a small subset of the total thermodynamical phase space or may require a
special set of initial conditions making it near impossible to form using brute
force simulation techniques. In this work we present state of the art machine
learning methods for the discovery of meta-stable phases across the
thermodynamical landscape.
Keywords: Machine Learning, Metastable, Phase Diagram
Presenting authors email: tloeffler@anl.gov