Using experimental data in computationally guided rational design of inorganic materials with machine learning

Abstract

While the impact of machine learning (ML) has been felt everywhere, its effect has been most transformative where large, high-quality datasets are available. For promising materials spaces, such as transition metal coordination complexes and metal–organic frameworks, the large chemical diversity has not yet been matched by similarly large datasets, and computational datasets (e.g., from density functional theory) may not be predictive. Extraction of experimental data from the literature represents an alternative approach to the data-driven design of materials. This perspective will describe efforts in (i) extracting experimental data; (ii) associating extracted data with known chemical structures; (iii) leveraging data in ML and screening; (iv) designing materials with enriched stability; and (v) using experimental data to improve high-throughput workflows. I will summarize some of the outstanding challenges and opportunities for data enrichment with high-throughput experimentation and large language models.