The marvels of modern technology can largely be attrib- uted to the discovery and characterization of new mate- rials. The discovery of semiconductors laid the foundation for modern electronics, while the formulation of new
molecules allows us to treat diseases previously thought
incurable. Looking into the future, some of the largest problems facing humanity now are likely to be solved by the discovery of new materials. In this article, we explore the techniques materials scientists are using and show how our novel
artificial intelligence system, Phase-Mapper, allows materials
scientists to quickly solve material systems to infer their
underlying crystal structures and has led to the discovery of
new solar light absorbers.
Phase-Mapper: Accelerating
Materials Discovery with AI
Junwen Bai, Yexiang Xue, Johan Bjorck, Ronan Le Bras, Brendan Rappazzo,
Richard Bernstein, Santosh K. Suram, R. Bruce van Dover,
John M. Gregoire, Carla P. Gomes
; From the stone age to the bronze, iron, and
modern silicon ages, the discovery and characterization of new materials has always been
instrumental to humanity’s development and
progress. With the current pressing need to
address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the
development of new materials that will allow for
renewable energy. To discover materials with the
required properties, materials scientists can perform high-throughput materials discovery, which
includes rapid synthesis and characterization via
X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the
phase map identification problem, involves the
determination of the crystal structure of materials from materials composition and structural
characterization data. This analysis is traditionally performed mainly by hand, which can take
days for a single material system. In this work
we present Phase-Mapper, a solution platform
that tightly integrates XRD experimentation, AI
problem solving, and human intelligence for
interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver,
AgileFD, which is based on convolutive nonnegative matrix factorization (NMF). AgileFD
allows materials scientists to rapidly interpret
XRD patterns, and incorporates constraints to
capture prior knowledge about the physics of the
materials as well as human feedback. With our
system, materials scientists have been able to
interpret previously unsolvable systems of XRD
data at the Department of Energy’s Joint Center
for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of
new solar light absorbers and is provided as an
illustrative example of AI-enabled high-throughput materials discovery.
