Who knew watching paint dry could be so exciting?! Materials that we use every day, from the paints on our houses to the glass on our iPhones, are designed in labs through physical experimentation. With a treasure trove of historical materials data meeting modern trends in data science, the materials industry is poised for an AI revolution. However, pulling scikit-learn off the shelf won’t always get you great results, because the physical processes underpinning experimentation and production introduce unique challenges that are not well solved by traditional AI and ML techniques.
This talk will review three challenges encountered during Citrine Informatics’s years of experience bringing AI to Materials Design, and will offer solutions with broad applicability to the physical sciences and beyond. Learn about how sequential learning can help mitigate “small data”, probabilistic graphical models can express decades of domain knowledge, and how uncertainty propagation is the key to expressing the instability inherent in physical processes.