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The automation of the laboratory necessitates aligning abstract theories of chemical processes with the technology which accomplishes them.
FREMONT CA: The automation of the laboratory includes associating the abstract conceptions of chemical processes with the hardware responsible for implementation. It is possible to achieve this by developing a fully attached virtual representation of the physical equipment and their status, that is, a digital twin of the laboratory that bridges the gap between the virtual and the real world. Thus, allowing the orchestration of physical and computational testing in cyberspace, lubricating chemical discovery automation. As a result, it reduces the time taken from developing a new chemical in a research setting to delivering mass manufacturing to end customers. Consequently, the digitalization of chemical manufacturing is one of the important technical avenues toward a more sustainable society, as it allows for significant decarbonization while reducing labour and energy consumption.
Researchers use automation of chemical experiments and improvements in machine learning to enable functional material discovery, chemical reaction discovery, synthesis planning, and process condition optimization. Despite the community's considerable success, the time and work required to integrate new equipment into a current platform can be costly. For each piece of equipment introduced, customized extraction–transformation–loading (ETL) tools and a specialized data exchange architecture for establishing successful communication will be established. Thus, these platforms typically suffer scalability and interoperability challenges because of various data formats that act as a barrier to holistic integration, particularly when it comes to the vision of a worldwide connected collaboration.
In order to achieve laboratory automation, an effective way to communicate and share data should be addressed apart from the interoperable data illustration.
This perspective focuses on examining the potential for emerging technology to improve the way of approaching laboratory automation. The following is how this point of view is presented. Initially, looking at the current level of laboratory automation, with a particular focus on data infrastructure, and later assessing community initiatives toward standardised data representation and successful data exchange based on the constraints of current options. With this approach the intelligent automation of experiments can be connected with chemical knowledge resources and can be oriented with AI techniques.