## Main

### Successful examples of SDLs

Over the past five years, proof-of-concept SDLsfor example, Chemputer13,20, BEAR36,47, CAMEO28 and Artificial Chemist22,23were successfully utilized for the autonomous synthesis of nanoparticles22,23,24,25,26,27,32,34, polymers48 and copolymers49, thin-film materials29,50,51, carbon nanotubes52, supramolecular clusters53, complex organic molecules13,19,20,54, photocatalysts21 and shape-memory materials28 for applications in additive manufacturing36, liquid product formulations55,56, pharmaceuticals57 and clean energy technologies58,59,60. Figure 4 shows three approaches to the hardware and robotic integration of SDLs: portable robotic arms that access an entire SDL (Fig. 4a)21 or connect different modules of SDLs61, stationary robots that supply manufactured parts36, collected nanomaterial inks26 or thin film substrates (Fig. 4b)29,30 to different SDL modules, and compact workstations for tube and/or pump-based reagent transfer between the synthesis and characterization modules of SDLs (Fig. 4c)13,62. The unique aspect of mobile robots (Fig. 4a) is the facile access to conventional characterization techniques available in a chemical lab without the need for a direct integration with the synthesis module of SDLs. Despite this advantage, the high cost of mobile robots that offer a precise and reproducible sample transfer with multiple grippers poses a major bottleneck for such SDLs.

Figure 5 shows examples of parallel batch (Fig. 5a)25 and flow reactors (Fig. 5b)22,23,24,26,34 utilized to automatically perform reactions in SDLs. In the case of organic or nanomaterial synthesis with no solid reagent or precipitation during the synthesis, flow reactors provide an excellent opportunity for reaction miniaturization, reduced chemical consumption and waste generation, facile integration with online characterization techniques and access to synthesis conditions, for example, mixing and heating or cooling rates that are not accessible to batch reactors19,22,23,24,32,63. These advantages of flow reactors make them a promising candidate to access unexplored regions of the design spaces for emerging molecules and (nano)materials. For solid-phase synthesis and processing (for example, preparation of thin films, battery materials or solid-state polymerization), or reactions with the precipitation of solid products or by-products, parallel batch reactors are more suitable reactor candidates for SDLs.

From the characterization perspective, both online and offline modules, such as custom-developed spectroscopy techniques22,23,24,32,34 and imaging tools29,30, and off-the-shelf analytical units, for example, high-performance liquid chromatography, Fourier-transform infrared spectroscopy, NMR spectroscopy and gas chromatography13,19,20,21, have been successfully integrated with SDLs for the autonomous synthesis and development of functional materials and molecules. Furthermore, online characterization modules can provide access to measurements after each stage of multistage syntheses or material fabrication. Such intermediate-stage information can be leveraged to accelerate a search through the high-dimensional space of multistage processes by the early identification of more advantageous synthetic routes. The integration of SDLs with online characterization techniques leverages the extensive hardware development and online reaction sampling techniques developed during the past two decades via the emergence and growth of lab-on-a-chip technologies. In addition to common spectral characterization techniques, the structural characterization of fabricated (nano)materials using electron microscopy (transmission electron microscopy and scanning electron microscopy) and small- and wide-angle X-ray scattering can also be integrated with SDLs; however, the high capital cost and the need for additional complex hardware development and integration limit their integration with SDLs to specially dedicated facilities. From the ML perspective, a range of strategies suitable for handling continuous and discrete parameters, from Bayesian optimization to evolutionary algorithms (for example, covariance matrix adaptation evolution strategy and genetic algorithms) have been successfully implemented in SDLs for the accelerated development and on-demand synthesis of organic molecules, nanomaterials and thin-film materials. For details of different ML algorithms utilized in SDLs relevant to chemical and materials sciences, we refer the reader to recent comprehensive reviews of such algorithms40,64,65,66,67,68.

## Current limitations and future opportunities of SDLs

Despite successful proof-of-concept examples of SDLs in the accelerated synthesis of complex organic molecules and advanced (nano)materials, many opportunities exist for further research and development. First and foremost, for non-experts in autonomous robotic experimentation, the transition of SDLs from sophisticated custom-developed technologies to a mainstream approach in experimental chemical and materials sciences requires major advances in hardware development, which include module engineering and online characterization techniques to reduce the entry barriers, such as cost, module assembly, operation and troubleshooting. The high cost of robots and characterization modules, the complicated assembly of custom-developed modules and extensive troubleshooting, all combined with the lack of standardization of hardware modules, data flow, data representation and intelligent experiment-selection algorithms, are the current major limitations of SDLs. We see the initial cost barrier of SDLs as an enabling opportunity for the research acceleration community in chemical and materials sciences. The large capital expenditure of current SDLs provides a unique opportunity for researchers interested in hardware development to focus on low-cost and open-source SDL modules, such as liquid-handling robots69, syringe pumps70, three-dimensional-printed reactionware71 and field-deployable diagnostics72. Moreover, the recent growth of cloud labs around the world73 provides another potential avenue for early career researchers to access state-of-the-art robotic experimentation facilities without major capital investments.

The adoption of SDLs by scientists across chemical and materials sciences would entail a highly intelligent and flexible automation of research labs with autonomously reconfigurable experimental modules. The challenge of the autonomous development of advanced functional materials, in contrast to that of small molecules, is the lack of reproducible data in the literature. Although automated data extraction from the literature, despite a proved bias74, has been achieved for organic synthesis19,75 and successfully enabled data-driven retrosynthesis or highly accurate reaction prediction, it has largely failed for advanced (nano)materials. This failure, however, creates a unique opportunity for SDLs. The sparse data availability for advanced (nano)materials (for example, clean energy materials), in combination with their lab-to-lab variations, makes SDLs an ideal research platform to provide reproducible data for ML modelling and design space navigation and for knowledge transfer within each class of targeted material. In general, SDLs improve the experimental data reproducibility through digitization, enhanced accuracy, transferrable knowledge and minimization of the impact of human errors.

Although mobile or stationary robotic arms can be utilized for the transfer of liquid-phase reagents or products between different modules or the automatic reconfiguration of SDLs, they are mostly required for SDLs that handle solid-phase reagents, or in cases for which more powerful characterization techniques, for example, NMR spectroscopy, are required. A critical requirement of SDLs working with solid-phase reactions, reagents or samples is the need to use robotics for precise solid-powder dosing and a fast and reliable sample transfer between different SDL modules. Despite the rapid progress of robots and solid-dispensing technologies over the past two decades, the high cost of precise solid-dispensing and robotic arms, with the required precision, reproducibility, mobility and speed, poses a limitation for the widespread implementation of SDLs. Reductions in the costs of solid- and/or liquid-dispensing and stationary and/or mobile robots are enabling factors for the broad deployment and adoption of SDLs across chemical and materials sciences. We believe that a critical next step for SDL adoption is the development of cost-effective mobile robotic manipulators designed to enable flexibility in the automatic reconfiguration of the SDL design and adaptation to dynamic changes in the workspace. Furthermore, robotic manipulators should provide precise and reproducible high-speed operations to maximize the reproducibility and agility of SDLs. A reduced cost of mobile robotic manipulators would enable the incorporation of multiple robots in the SDLs, which would prevent disruption in the closed-loop SDL operation in the case of a potential failure of a specific robot. Such open-access and mobile robotic manipulators will be able to make agile actions in an environment, similar to conventional human-centred research labs, without the need for a special lab space design or modification of the SDL operation.

An important software aspect of SDLs is their robust and flexible integration with ML to provide autonomy for navigation through the design space of molecules and materials. The rapidly growing list of ML modelling and experiment selection strategies makes the algorithm selection a challenging task for non-experts. This challenge is an exciting opportunity for the future development of SDLs towards the standardization of ML algorithms suitable for different end-to-end experimental workflows, operation modes (exploration, exploitation or mechanistic studies) and targeted classes of molecules or (nano)materials (for example, prior knowledge versus physics-based models versus black-box search).

Industry plays an important role in addressing the hardware and software challenges for SDLs by leveraging the prior advancements in the development of experimental tools for combinatorial screening applications in medicinal chemistry and molecular biology. By focusing on cost reduction and the standardization of robots, experimental modules and characterization techniques for SDLs, industry can reduce the entry barrier to SDLs for scientists. Standard experimental modules and equipment communication protocols are a critically important advancement for future SDLs39,76. The main pieces of equipment for the online or in situ characterization of materials or molecules using conventional spectroscopy and chromatography techniques already exist. However, SDLs generally need to use custom-built hardware (for example, a flow cell for the in situ monitoring of reactions performed in a flow reactor) or a triggering method (for example, online gas chromatography sampling) to integrate the existing characterization units with other SDL modules. As the number of SDL users increases, it is expected the companies that manufacture characterization instrumentation, such as spectrometers and chromatographs, as well as NMR spectroscopy, mass spectrometry and X-ray diffraction equipment, will focus on the design and development of sampling and integration modules with open-access software for the in situ and online characterization of materials and molecules. In addition, the leading SDL research groups around the world are strongly encouraged to work with instrumentation companies to expand the available in situ and online characterization modules. A successful example of such an academia–industry collaboration in the advancements of online reaction monitoring modules is the powerful ReactIR probe for integration with flow reactors developed by Mettler Toledo in collaboration with the Ley group at the University of Cambridge77.

We encourage the ML community in chemical and materials sciences to focus their future efforts on the facile benchmarking of application-specific algorithms45, expanding open-access databases and making the design space exploration and/or exploitation software user-friendly. Another important aspect of SDLs that is still not well studied is how to carefully choose the best ML algorithm to generate new fundamental knowledge about an underlying phenomenon or an unexpected relationship between input parameters and output properties for the class of reactions or materials explored by the SDL. As the number of experimental modules and independent input parameters of SDLs increases over the next few years, more data- and/or physics-informed ML strategies will be needed to reduce the total cost of computation and experimentation to discover new materials and molecules or the sustainable way to manufacture them at scale78,79,80. Such information can be provided to the SDL either from open-source reaction databases81, or by ML models that are created using prior data generated by the same or another SDL (for example, the model built on a different subset of materials or reactions from the same general class of materials or reactions)82. Data- and/or physics-informed autonomous experimentation is a necessary next step of the SDL’s software development to realize their largest impact in the autonomous discovery of materials and molecules. This aspect of future SDLs requires cross-disciplinary training83 and collaboration between the ML and chemical and materials science communities to enable implementation of the most suitable ML algorithms that are accessible and understandable to non-experts. Such collaborations are necessary to accelerate the intelligent search through the chemical space with constrains, metrics and objectives defined by domain experts.

One of the most intriguing aspects of SDLs, which is largely unexplored and directly tied to the future hardware and software advancements, is their remote operation capabilities through the cloud or remote connection to define the next goal of the SDL operation27. Automatic access to a library of starting reagents, in combination with reliable and reproducible automated sample preparation, synthesis and online and offline characterization techniques substantially reduces the required amount of ‘in-person’ presence of the researcher in the lab during the SDL operations. Furthermore, the remote operation of SDLs in different physical locations provides the unique advantage of reproducible knowledge-sharing (data fusion) opportunities via open databases for different classes of emerging materials and molecules.

We note that the remote operation of SDLs will require different workforce training than that of the current paradigm in chemical and material sciences. The rapidly emerging remote connectivity tools, such as virtual reality84 and augmented reality85, along with digital communication platforms provided stimulating avenues to explore for future SDLs and workforce development during the pandemic and continued thereafter. As SDLs start to penetrate different applications of experimental sciences, one of the major challenges in the next decade will be the required talent pool of a new generation of interdisciplinary trained scientists to utilize SDLs to their full potentials. The need for this new generation of scientists will require us to re-evaluate our student’s training and focus on multidisciplinary skills in academia.