Since 2022 the focus of AI has been mostly driven by large language models(LLMS) (LLMs) and generative AI and advancing them to concepts like artificial general intelligence (AGI) and eventually artificial superintelligence (ASI). I don't know what the time frame looks like for AGI and ASI or even if they are achievable with transformer and reinforcement learning techniques, given its not clear if these work because of data scaling or reward policy. What I'm more interested in for this year is I think the maturity of LLMs and the onset of agentic AI we are going to see a lot more activity in autonomous robotics. As a consequence, the availability of autonomous robotics tied to a materials synthesis and characterization lab will be come more more practical.
This shift from human in the lab to robot in the lab is going to change how scientist conduct their work and experiments. The reason being is small R&D iterative experiments will rely less on human intuition and more on data driven approaches that guide autonomous robots and systems.
 |
| Checkerboard of autonomy levels for labs (adapted from figure 1 in ref. [5]) |
Due to the nature of chemistry synthesis and characterization, some of the most early examples of autonomous robotics occurred with optimizing analytical procedures for spectroscopic detection [5]. These systems performed low supervision (i.e., limited human intervention) experimental tasks planned by data-driven approaches, such as maximizing the intensity of a characteristic absorbance peak. However, we are now entering an era, at least I believe, where self-driving labs (SDLs) will become a staple in small R&D labs.
Series Focus π€π₯Ό
This is a series of posts on the topic of self-driving labs that I write about this year, at least thats the plan. The particular focus of this post is more on the robotic manipulator aspect of SDLs as opposed to automation of equipment through microcontrollers or software. For example automating the control of an SEM through IO interfaces would not not be a robotics implementation, unless your utilizing a humanoid robot to interface with the keyboard/mouse to perform the task; not something I'm really referencing in this post nor do I think we are anywhere close to that.
Objectives for Robotics in Labs
In short the basic idea of autonomous robotics in the lab and thereby self-driving laboratories (SDLs)1 revolves around the optimization of robotic manipulators' actions through a data-driven approach. The systems form a closed loop where data is continuously collected, analyzed, and used to guide the next set of actions. For instance, in the work by Abolhasani et al. [1], AI/ML are integrated into SDLs to form hypotheses for experimentation, with the output being shared datasets and publications. This closed-loop process ensures that data analysis informs the actions of robotic manipulators in the SDL, optimizing the experimental workflow.
Another approach, as discussed by Bai et al. [2], is a goal-driven architecture where researchers set goals/objectives and resource restrictions2, triggering a closed-loop process. The use of AI/ML propose new experiments, translating conditions into machine-actionable recipes that control the hardware for reaction and characterization. This iterative process ensures that the system's actions are continuously optimized based on data analysis and comparison to research goals.
To give some insight to an inorganic materials problem, Chen et al. [3] demonstrated a thermodynamic strategy in a robotic enabled synthesis lab. The SDL navigates high-dimensional phase diagrams to maximize reaction energy and achieve desired phase purity. The AutoSciLab framework by Desai et al. [4], uses active learning3 to efficiently design experiments by creating hypotheses in a latent space. The use of active learning enables the system to "focus" on the most informative data points in the experimental space. As a result this can greatly reduce the number of experiments needed to achieve a desired result.
These SDLs aren't without their challenges and the robotic manipulation although amazing still will need to have the close to the human capabilities of being nimble and adjustable. Current robotic manipulators have significant degrees-of-freedom and fine motor capabilities. However, the ability to perform complex dynamic tasks with high precision and adaptability is still challenging. There is also the need to create benchmark and performance metrics for SDLs as highlighted by Volk et al. [6]. The authors emphasized the importance of reporting parameters and performance metrics to guide research in SDLs. This is absolutely necessary to be able to compare the performance of different SDLs. This data is needed to aid and inform decision-making of future SDL implementations.
While the terminology may vary for autonomous robotics in labs, the overarching objective remains consistent: to delineate an autonomous robotic process that leverages data-driven methodologies to optimize specific experimental goals. Examples of this include maximizing reaction energy, achieving phase purity, and enhancing spectroscopic signals. By employing closed-loop systems, SDLs continuously collect and analyze data, allowing robotic manipulators to adapt their actions based on real-time insights. This iterative approach not only streamlines the experimental workflow but also fosters a more efficient and effective research environment, ultimately advancing the landscape of materials synthesis and characterization.
Footnotes
-
A pretty good list of review and original research papers on self-driving labs can be found at awesome-self-driving-labs. Its not comprehensive but its a good start. ↩
-
This is important because despite a SDL or autonomous systems potential for carrying out actions, it is very much the case that the actual repeated resource(s) is limited. Here resource could be a very expensive precursor for synthesis or timely characterization. Another way to think of this is as the time the robot has to perform the experiment, the amount of energy the robot has to perform the experiment, the amount of money the robot has to perform the experiment, etc. ↩
-
Active learning is a ML technique where a model is trained by selecting (through external query) the most informative data points for learning, where as traditional ML approaches that use a fixed dataset. ↩
References
[1] M. Abolhasani, K.A. Brown, Guest Editors, Role of AI in experimental materials science, MRS Bulletin 48 (2023) 134–141. DOI.
[2] J. Bai, S. Mosbach, C.J. Taylor, D. Karan, K.F. Lee, S.D. Rihm, J. Akroyd, A.A. Lapkin, M. Kraft, A dynamic knowledge graph approach to distributed self-driving laboratories, Nat Commun 15 (2024) 462. DOI.
[3] J. Chen, S.R. Cross, L.J. Miara, J.-J. Cho, Y. Wang, W. Sun, Navigating phase diagram complexity to guide robotic inorganic materials synthesis, Nat. Synth 3 (2024) 606–614. DOI.
[4] S. Desai, S. Addamane, J.Y. Tsao, I. Brener, L.P. Swiler, R. Dingreville, P.P. Iyer, AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery, (2024). DOI.
[5] G. Tom, S.P. Schmid, S.G. Baird, Y. Cao, K. Darvish, H. Hao, S. Lo, S. Pablo-GarcΓa, E.M. Rajaonson, M. Skreta, N. Yoshikawa, S. Corapi, G.D. Akkoc, F. Strieth-Kalthoff, M. Seifrid, A. Aspuru-Guzik, Self-Driving Laboratories for Chemistry and Materials Science, Chem. Rev. 124 (2024) 9633–9732. DOI.
[6] A.A. Volk, M. Abolhasani, Performance metrics to unleash the power of self-driving labs in chemistry and materials science, Nat Commun 15 (2024) 1378. DOI.
