A Modular Programmable Inorganic Cluster Discovery Robot for the Discovery and Synthesis of Polyoxometalates

Abstract

The exploration of complex multicomponent chemical reactions leading to new clusters, where discovery requires both molecular self-assembly and crystallization, is a major challenge. This is because the systematic approach required for an experimental search is limited when the number of parameters in a chemical space becomes too large, restricting both exploration and reproducibility. Herein, we present a synthetic strategy to systematically search a very large set of potential reactions, using an inexpensive, high-throughput platform that is modular in terms of both hardware and software and is capable of running multiple reactions with in-line analysis, for the automation of inorganic and materials chemistry. The platform has been used to explore several inorganic chemical spaces to discover new and reproduce known tungsten-based, mixed transition-metal polyoxometalate clusters, giving a digital code that allows the easy repeat synthesis of the clusters. Among the many species identified in this work, the most significant is the discovery of a novel, purely inorganic W 24 Fe III –superoxide cluster formed under ambient conditions. The modular wheel platform was employed to undertake two chemical space explorations, producing compounds 1 – 4 : (C 2 H 8 N) 10 Na 2 [H 6 Fe(O 2 )W 24 O 82 ] ( 1 , {W 24 Fe}), (C 2 H 8 N) 72 Na 16 [H 16 Co 8 W 200 O 660 (H 2 O) 40 ] ( 2 , {W 200 Co 8 }), (C 2 H 8 N) 72 Na 16 [H 16 Ni 8 W 200 O 660 (H 2 O) 40 ] ( 3 , {W 200 Ni 8 }), and (C 2 H 8 N) 14 [H 26 W 34 V 4 O 130 ] ( 4 , {W 34 V 4 }), along with many other known species, such as simple Keggin clusters and 1D {W 11 M 2+ } chains.

Applied Machine Learning for Developing Next‐Generation Functional Materials

“…Similar works have also been conducted for other material classes/applications such as polyoxometalates, [21] nanoparticles, [22] thin films, [23] piezoelectrics, [24] and photocatalysts. [25] As highlighted above, this combination of robotics and machine learning bypasses human bias and labor limitations, leading to objective and rapid material discovery/synthesis.…”

“…While an ML guided retrospection of experimental work offers an avenue to missed insights, many research groups are now looking to leverage the combined efficiency of robotics and machine learning to automate future material discovery and synthesis. [4,19,[21][22][23][24][25] For example, Chan, et al have harnessed the power of robotics to synthesize 8172 different metal halide perovskites based on 45 different organic ammonium cations using inverse temperature crystallization (ITC). [19] This robotaccelerated perovskite investigation and discovery ("RAPID"), led to a fivefold increase in the number of metal halide perovskites synthesizable via ITC and was used to train an ML model capable of predicting likelihood of single crystal formation of such perovskites for future synthesis.…”

Robotic Stepwise Synthesis of Hetero-Multinuclear Metal Oxo Clusters as Single-Molecule Magnets

“…Therefore, we utilized the MWP where these parameters can be digitized and accurately delivered to our reaction vessels. The system itself uses in house software, written in Python to control hardware via Arduino to run the operations of the synthesis . The MWP-assisted screening was attempted using the following stock solutions: acetonitrile, acetonitrile solution of I (8.32 mM), acetonitrile solution of AOTf (A = Li, Na, K, Ag; 50 mM), and diethyl ether (poor solvent) (Figure ).…”

“…The platform used to perform these procedures was developed in the Cronin group at the University of Glasgow. The platform was published in 2020 . Full details of the platform can be found in the Supporting Information of that previous work, including the bill of materials and instructions for the control software and construction of the platform.…”

“…A number of automated synthesis methods have been reported in recent years for a variety of synthetic systems . More recently, a modular system for the combinatorial exploration of inorganic chemical space has been developed in the Cronin group at the University of Glasgow named the Modular Wheel Platform (MWP) . This system has proven to be a powerful tool for automated screening of reaction conditions, leading to the discovery of gigantic and novel POMs.…”