High-fidelity computer-aided
experimentation is becoming more accessible
with the development of computing power and artificial intelligence
tools. The advancement of experimental hardware also empowers researchers
to reach a level of accuracy that was not possible in the past. Marching
toward the next generation of self-driving laboratories, the orchestration
of both resources lies at the focal point of autonomous discovery
in chemical science. To achieve such a goal, algorithmically accessible
data representations and standardized communication protocols are
indispensable. In this perspective, we recategorize the recently introduced
approach based on Materials Acceleration Platforms into five functional
components and discuss recent case studies that focus on the data
representation and exchange scheme between different components. Emerging
technologies for interoperable data representation and multi-agent
systems are also discussed with their recent applications in chemical
automation. We hypothesize that knowledge graph technology, orchestrating
semantic web technologies and multi-agent systems, will be the driving
force to bring data to knowledge, evolving our way of automating the
laboratory.
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