Abstracts Book - IMRC 2018

• SD2-O031 Invited Talk
MACHINE-LEARNING OF CRYSTAL STRUCTURE ENERGY
LANDSCAPES
Richard Hennig 1
1 University of Florida, Materials Science and Engineering, United States.
Evolutionary algorithms provide an efficient method to explore the global
energy landscape of materials and identify low-energy minima but usually rely
on density-functional theory codes to perform such calculations at high
computational cost. To accelerate the structure search, it is desirable to utilize
the data obtained during structural relaxations through the learning of a
surrogate model. We present a machine learning approach for the formation
energies of crystal structures. We explore two types of machine-learning
techniques: kernel-based learning algorithms and artificial neural networks. The
efficiency of machine learning approaches relies on suitable data
representations that encode the relevant physical information about the crystal
structures. We test several physically motivated structure representations. We
show that machine learning of the energy landscape using representations that
encode the radial and angular distribution functions predict the formation
energies of Li-Ge crystals with chemical accuracy and a small prediction error of
20 meV/atom across the composition and structure space. The accuracy
demonstrates the potential of machine-learning models to reduce the
computational cost needed to identify low-energy candidate structures and
improve the performance of the genetic algorithm for structure predictions.
Acknowledgment:
Supported by NSF awards 1440547, 1549132, and 1748464.
Keywords: machine learning, density-functional theory, genetic algorithms
Presenting authors email: rhennig@ufl.edu