Research Highlighted by Chemistry of Materials Journal
Virtual Issue on Machine-Learning Discoveries in Materials Science
From Chemistry of Materials:
Applications of machine learning, and machine learning-based models in materials chemistry, are a rapidly
growing area of research. Traditional methods of exploration
are effective but tedious and miss a vast degree of parameter
space due to limitations of time and resources. While scientists
tend to shy away from using the expression “paradigm shift”
due to overuse, data-driven science could actually be the real
deal; data-driven science has been termed the fourth paradigm
shift after empirical science (the first), model-based theoretical
science (the second), and computational science (the third).
Materials science has been a natural area for the growth of
data-driven science, as evidenced by the Materials Genome
Initiative, and open data resources like The Materials Project.
Historically, the great wealth of scientific expertise, experience,
and knowledge we take for granted was planned, recorded, and
catalogued by previous generations of researchers in scientific
reports and articles. The challenge faced today is that, in the
era of digitalization of information, it is impossible to process
these big data without assistance from computer algorithms.
Machine learning enables the discovery of trends in chemical
data and provides guidance for new materials via fast screening
of unexplored chemical white space. The next ground-breaking
discovery of a high-efficiency thermoelectric or a roomtemperature superconductor may happen thanks to insights
from machine learning, and it is exciting to see a growing
number of submissions to Chemistry of Materials that view
machine learning as a helpful tool for materials discovery. In
the current virtual issue, we highlight 22 recent publications, that employ machine-learning methods to target new
materials, optimize properties, and predict potentially interesting candidates to synthesize in the laboratory.
Accompanying the growth of interest in machine learning is
the emergence of newly developed machine-learning methods,
which are also reflected in this virtual issue.