Conspectus
We must accelerate the pace at which we make
technological advancements
to address climate change and disease risks worldwide. This swifter
pace of discovery requires faster research and development cycles
enabled by better integration between hypothesis generation, design,
experimentation, and data analysis. Typical research cycles take months
to years. However, data-driven automated laboratories, or self-driving
laboratories, can significantly accelerate molecular and materials
discovery. Recently, substantial advancements have been made in the
areas of machine learning and optimization algorithms that have allowed
researchers to extract valuable knowledge from multidimensional data
sets. Machine learning models can be trained on large data sets from
the literature or databases, but their performance can often be hampered
by a lack of negative results or metadata. In contrast, data generated
by self-driving laboratories can be information-rich, containing precise
details of the experimental conditions and metadata. Consequently,
much larger amounts of high-quality data are gathered in self-driving
laboratories. When placed in open repositories, this data can be used
by the research community to reproduce experiments, for more in-depth
analysis, or as the basis for further investigation. Accordingly,
high-quality open data sets will increase the accessibility and reproducibility
of science, which is sorely needed.
In this Account, we describe
our efforts to build a self-driving
lab for the development of a new class of materials: organic semiconductor
lasers (OSLs). Since they have only recently been demonstrated, little
is known about the molecular and material design rules for thin-film,
electrically-pumped OSL devices as compared to other technologies
such as organic light-emitting diodes or organic photovoltaics. To
realize high-performing OSL materials, we are developing a flexible
system for automated synthesis via iterative Suzuki–Miyaura
cross-coupling reactions. This automated synthesis platform is directly
coupled to the analysis and purification capabilities. Subsequently,
the molecules of interest can be transferred to an optical characterization
setup. We are currently limited to optical measurements of the OSL
molecules in solution. However, material properties are ultimately
most important in the solid state (e.g., as a thin-film device). To
that end and for a different scientific goal, we are developing a
self-driving lab for inorganic thin-film materials focused on the
oxygen evolution reaction.
While the future of self-driving
laboratories is very promising,
numerous challenges still need to be overcome. These challenges can
be split into cognition and motor function. Generally, the cognitive
challenges are related to optimization with constraints or unexpected
outcomes for which general algorithmic solutions have yet to be developed.
A more practical challenge that could be resolved in the near future
is that of software control and integration because few instrument
manufacturers d...