  The availability of tera-byte scale experiment data calls for AI driven
approaches which automatically discover scientific models from data.
Nonetheless, significant challenges present in AI-driven scientific discovery:
(i) The annotation of large scale datasets requires fundamental re-thinking in
developing scalable crowdsourcing tools. (ii) The learning of scientific models
from data calls for innovations beyond black-box neural nets. (iii) Novel
visualization and diagnosis tools are needed for the collaboration of
experimental and theoretical physicists, and computer scientists. We present
Phase-Field-Lab platform for end-to-end phase field model discovery, which
automatically discovers phase field physics models from experiment data,
integrating experimentation, crowdsourcing, simulation and learning.
Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the
annotation time (by ~50-75%), while increasing annotation accuracy compared to
baseline; (ii) an end-to-end neural model which automatically learns phase
field models from data by embedding phase field simulation and existing domain
knowledge into learning; and (iii) novel interfaces and visualizations to
integrate our platform into the scientific discovery cycle of domain
scientists. Our platform is deployed in the analysis of nano-structure
evolution in materials under extreme conditions (high temperature and
irradiation). Our approach reveals new properties of nano-void defects, which
otherwise cannot be detected via manual analysis.
