Scientific AI in materials science: a path to a sustainable and scalable paradigm - IOPscience

Recent reports, reviews, symposia, and workshops have heralded machine learning (ML) and artificial intelligence (AI) methods as the next scientific paradigm in materials discovery and optimization [1–5]. Applications to materials science have exploded, spanning data analysis, knowledge extraction, and experiment selection [1, 6–9]. The numerous reasons for this trend are related to the omnipresence of ML systems in our everyday lives, the free availability software, and the demonstrated successes in materials discovery and on-the-fly data acquisition inspired by the Materials Genome Initiative (MGI) [1, 10–12]. However, despite their recent prominence, these techniques have been applied in a variety of materials science fields since the early 1960's [13–17]. Some recent examples of the successful implementation of ML to materials science were demonstrated by the high-throughput experimental (HTE, also known as'combinatorial') community. Parallel material synthesis and rapid characterization introduces a critical bottleneck in the analysis of hundreds to thousands of high-quality measurements correlated in composition, processing and microstructure [18–21]. There have been several international efforts to standardize data formats and create data analysis and interpretation tools for large scale data sets [22–24].