Synthesizing the best material globally is challenging;
it needs
to know what and how much the best ingredient composition should be
for satisfying multiple figures of merit simultaneously. Traditional
one-variable-at-a-time methods are inefficient; the design-build-test-learn
(DBTL) method could achieve the optimal composition from only a handful
of ingredients. A vast design space needs to be explored to discover
the possible global optimal composition for on-demand materials synthesis.
This research developed a hypothesis-guided DBTL (H-DBTL) method combined
with robots to expand the dimensions of the search space, thereby
achieving a better global optimal performance. First, this study engineered
the search space with knowledge-aware chemical descriptors and customized
multiobjective functions to fulfill on-demand research objectives.
To verify this concept, this novel method was used to optimize colorimetric
ammonia sensors across a vast design space of as high as 19 variables,
achieving two remarkable optimization goals within 1 week: first,
a sensing array was developed for ammonia quantification of a wide
dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art
detection limit of 50 ppb was reached. This work demonstrates that
the H-DBTL approach, combined with a robot, develops a novel paradigm
for the on-demand optimization of functional materials.