End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning


  • Md Nasim Purdue University
  • Xinghang Zhang Purdue University
  • Anter El-Azab Purdue University
  • Yexiang Xue Purdue University




Scientific Discovery , Machine Learning , Multidisciplinary Topics and Applications , Physics, Track: Deployed Innovative Tools


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 & 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.



How to Cite

Nasim, M., Zhang, X., El-Azab, A., & Xue, Y. (2024). End-to-End Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23005-23011. https://doi.org/10.1609/aaai.v38i21.30342



IAAI Technical Track on Deployed Innovative Tools for Enabling AI Applications