| Abstract Scope |
One of our research interests involve development of efficient data-driven strategies for navigating the vast search space of material possibilities. We asked the following question, is there a relationship between ML model quality, utility functions, and the rate at which optimal materials are discovered? Our on-going empirical work appear to indicate that the rate of discovery is dictated by the nuances of the composition–property landscape. Having poor ML models does not equate to poor research outcomes, provided appropriate input descriptors are included that capture the structure-property relationships. Further, utility functions that evaluate the exploration-exploitation tradeoff do not always produce a “winning” search strategy. Examples will be discussed that highlight the non-trivial nature of adaptive machine learning in materials science domain. |