Validating Predictions in Materials Science

This chapter explores the validation process of new material predictions generated by a model, emphasizing both automated checks and necessary experimental verification. The discussion covers innovative validation methods using natural language processing (NLP) to identify promising thermoelectric materials, highlighting a significant correlation between NLP predictions and actual research findings. It also acknowledges the limitations in current approaches while contemplating the potential for discovering unconventional materials beyond low-hanging fruit.