Towards End-to-End Structure Solutions from Information-Compromised
  Diffraction Data via Generative Deep Learning
Gabe Guo Judah Goldfeder Ling Lan Aniv Ray Albert Hanming Yang Boyuan Chen Simon JL Billinge Hod Lipson
The revolution in materials in the past century was built on a knowledge of
the atomic arrangements and the structure-property relationship. The sine qua
non for obtaining quantitative structural information is single crystal
crystallography. However, increasingly we need to solve structures in cases
where the information content in our input signal is significantly degraded,
for example, due to orientational averaging of grains, finite size effects due
to nanostructure, and mixed signals due to sample heterogeneity. Understanding
the structure property relationships in such situations is, if anything, more
important and insightful, yet we do not have robust approaches for
accomplishing it. In principle, machine learning (ML) and deep learning (DL)
are promising approaches since they augment information in the degraded input
signal with prior knowledge learned from large databases of already known
structures. Here we present a novel ML approach, a variational query-based
multi-branch deep neural network that has the promise to be a robust but
general tool to address this problem end-to-end. We demonstrate the approach on
computed powder x-ray diffraction (PXRD), along with partial chemical
composition information, as input. We choose as a structural representation a
modified electron density we call the Cartesian mapped electron density (CMED),
that straightforwardly allows our ML model to learn material structures across
different chemistries, symmetries and crystal systems. When evaluated on
theoretically simulated data for the cubic and trigonal crystal systems, the
system achieves up to $93.4\%$ average similarity with the ground truth on
unseen materials, both with known and partially-known chemical composition
information, showing great promise for successful structure solution even from
degraded and incomplete input data.