Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

Research publications are the major repository of scientific knowledge. However, their
unstructured and highly heterogenous format creates a significant obstacle to large-scale …

A bottleneck in efficiently connecting new materials discoveries to established literature has
arisen due to an increase in publications. This problem may be addressed by using named …

Data science and machine learning have the potential to accelerate the discovery of
effective catalysts; however, these approaches are currently held back by the issue of …

Metal halide perovskites (MHPs) have catapulted to the forefront of energy research due to
the unique combination of high device performance, low materials cost, and facile solution …

Abstract Machine learning is rapidly becoming an integral part of experimental physical
discovery via automated and high‐throughput synthesis, and active experiments in …

Digitizing large collections of scientific literature can enable new informatics approaches for
scientific analysis and meta-analysis. However, most content in the scientific literature is …

Materials science literature contains millions of materials synthesis procedures described in
unstructured natural language text. Large-scale analysis of these synthesis procedures …

Leveraging new data sources is a key step in accelerating the pace of materials design and
discovery. To complement the strides in synthesis planning driven by historical …

There currently exist no quantitative methods to determine the appropriate conditions for
solid-state synthesis. This not only hinders the experimental realization of novel materials …