Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

Abstract

We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed.In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki-Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction.While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers design their products with self-driving laboratories in mind. Challenges in motor function are largely related to handling heterogeneous systems, such as dispensing solids or performing extractions. As a result, it is critical to understand that adapting experimental procedures that were designed for human experimenters is not as simple as transferring those same actions to an automated system, and there may be more efficient ways to achieve the same goal in an automated fashion. Accordingly, for self-driving laboratories, we need to carefully rethink the translation of manual experimental protocols.

Figure 1

Diagram of the design–make–test–analyze cycle in our self-driving laboratory, showing the process for the development of new organic semiconductor laser materials.

Figure 2

Integration of ChemOS and its most important algorithms into the process or material optimization workflow.

Figure 3

Top: Photo of the Chemspeed deck. The inset shows the top of the ISYNTH with one of the drawers (vertical row of wells) highlighted. Bottom: (Right) Icons for liquid dispensing, solid dispensing, and solid-phase extraction actions. (Left) Diagram of the iSMcc process along with icons indicating where different capabilities are used. Cross-coupling (C): X-Ar-BMIDA (1 equiv), Ar–B(OH)₂ (3 equiv), Pd-XPhos G2 (5 mol %), K₃PO₄ (2 equiv), THF, 16 h, 65 °C. Purification (P): precipitation from hexanes/THF 3:1. Deprotection (D): aqueous NaOH (1 M), 20 min, room temperature.

Figure 4

Schematic diagram of our analysis, purification, and optical characterization setup. The gray box is a schematic diagram of how a specific HPLC fraction is selected for further evaluation and how its properties are measured. Absorption measurements are carried out in the “absorption” flow cell. Photoluminescence (PL), PL quantum yield (PLQY), and photodegradation rate measurements are carried out in the “emission” flow cell. PL lifetime is measured in the “PL lifetime” flow cell. Gray polygons represent valves with the number of ports corresponding to the number of sides, and arrows represent the directions of sample transport.

Figure 5

Autonomous robotic workflows can accelerate the discovery of solid-state inorganic materials using proxy experiments. These can then be used in conjunction with more accurate full experiments to perform multifidelity optimization of the inorganic materials.