Symposium G:
AI/Data Driven Materials Science and Technology

Progress of materials science and engineering had been restricted for decades in the 20th century as it generally requires a large amount of high-quality experimental data necessitating huge expense and human costs. The situation has been, however, drastically improved in the last decade by the development of machine learning or artificial intelligence (AI) combined with accurate first-principles calculations and high-throughput experiments based on combinatorial and automatic approaches. Large materials data produced by these techniques allow for finding out as-yet-undiscovered hidden rules, accelerating the progress of materials science. The symposium will highlight achievements and challenges from these fields of research including the demonstration of materials database based on first-principles calculations, AI-guided and/or data-driven materials discovery/synthesis, high-throughput experiments with automation and combinatorial methods, machine learning applied to materials science such as spectroscopy, imaging technology, and structure predictions.

Topics will include:

• Computational materials database

• High-throughput experiments involving combinatorial methods and automation

• Machine learning prediction of physical/chemical properties

• AI guided or data driven materials discovery/synthesis

• Spectroscopy and imaging technology (STEM, etc.) based on machine learning

Invited Speakers: