Investigation on the Removal of Trace Contaminants AsH3, H2Se, Cd, Asse, and PbSe Using Monometallic and Bimetallic Adsorbents

In order to tackle many of humanity’s most pressing challenges, scientific discovery needs to be substantially accelerated. The automation of research activities plays a big role in this, from ubiquitous software tools to “self-driving laboratories”. Recently, the idea of an “AI scientist” that can make Nobel-worthy discoveries has been introduced in this context [1]. We argue that the current platform-based “bottom-up” approaches are not sufficient to achieve and could even limit further development [2].

Therefore, we introduce a holistic lab automation framework as part of The World Avatar project, an all-encompassing digital twin based on dynamic knowledge graph. Its hierarchical and semantic structure allow for deep representation of knowledge across different domains and scales. We argue it necessary to further interoperability by widening the search and optimisation space to include managerial tasks in research labs as well as information on infrastructure and buildings. This way we can ensure cost effectiveness, improve reproducibility, and free up human resources for creative tasks. To achieve this, an unconventional “top-down” approach is applied to lab automation, taking a systems engineering perspective. This approach aims to integrate all aspects of lab work and its automation, contrasting the many isolated solutions available that – amongst others – increase the risk of manufacturer lock-in.

It enables the deployment of a multi-agent system in which humans are inherently embedded [1]. By considering the human-in-the-loop as well as the surrounding infrastructure, the definition of objectives can go beyond the optimisation of reaction conditions and include aspects of cost effectiveness, resource distribution and safety. A classical self-driving lab can be enhanced to derive its own goals, execute optimisation tasks in a distributed manner [2], consider its direct and indirect environment, and potentially integrate with complex multiscale models [3] to iteratively improve the digital twins’ comprehensiveness and validity.

References

[1] Kitano, H. Nobel Turing Challenge: creating the engine for scientific discovery. npj Syst. Biol. Appl. 7, 1–12 (2021).

[2] Bai, J. et al. From Platform to Knowledge Graph: Evolution of Laboratory Automation. J. Am. Chem. Soc. Au 2, 292–309 (2022).

[3] Rihm, S.D. et al. Fully Automated Kinetic Models Extend our Understanding of Complex Reaction Mechanisms. Chem. Ing. Tech. (2023).