The discovery of new gigantic molecules formed by self‐assembly and crystal growth is challenging as it combines two contingent events; first is the formation of a new molecule, and second its crystallization. Herein, we construct a workflow that can be followed manually or by a robot to probe the envelope of both events and employ it for a new polyoxometalate cluster, Na6[Mo120Ce6O366H12(H2O)78]⋅200 H2O (1) which has a trigonal‐ring type architecture (yield 4.3 % based on Mo). Its synthesis and crystallization was probed using an active machine‐learning algorithm developed by us to explore the crystallization space, the algorithm results were compared with those obtained by human experimenters. The algorithm‐based search is able to cover ca. 9 times more crystallization space than a random search and ca. 6 times more than humans and increases the crystallization prediction accuracy to 82.4±0.7 % over 77.1±0.9 % from human experimenters.
Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone.
The discovery of new gigantic molecules formed by self-assembly and crystal growth is challenging as it combines two contingent events;first is the formation of anew molecule, and second its crystallization. Herein, we construct aworkflow that can be followed manually or by ar obot to probe the envelope of both events and employ it for an ew polyoxometalate cluster,N a 6 [Mo 120 Ce 6 O 366 H 12 (H 2 O) 78 ]·200 H 2 O( 1) which has at rigonal-ring type architecture (yield 4.3 %b ased on Mo). Its synthesis and crystallization was probed using an active machine-learning algorithm developed by us to explore the crystallization space,t he algorithm results were compared with those obtained by human experimenters.T he algorithmbased search is able to cover ca. 9t imes more crystallization space than arandom search and ca. 6times more than humans and increases the crystallization prediction accuracy to 82.4 AE 0.7 %over 77.1 AE 0.9 %f rom human experimenters.
A novel metal ion-mediated reaction of pyridine-2-amidoxime has led to an 1:3 mononuclear Mn(III) complex containing the 2,4-bis(2-pyridyl)-1,3,5-triazapentanedienate(-1) ligand; the high-spin Mn III in the complex is "EPR silent" at X-band. Upon coordination of a ligand (L) to a metal center (M), the former's properties (acidity, electrophilic or nucleophilic character, susceptibility to reduction or oxidation, etc.) can be significantly altered and therefore its reactivity can be enhanced or inhibited; coordination to M can even enable a reaction that would otherwise not take place. Thus the reactivity chemistry of coordinated ligands is a "hot" research theme in contemporary transition-metal chemistry [1]. Oxime (C═N-OH) and oximate (C═N-O-) groups can bind an M in a variety of coordination modes and are thus ideal candidates for reactivity chemistry [2]. Nucleophilic reagents can add to the C-atom (a reaction that is promoted by coordination of the N-atom), whereas electrophilic reagents can attack the O-or the N-sites [2]. Scheme 1. Structural formulae and abbreviations of the ligands discussed in the text. The initially used ligand is pyridine-2-amidoxime, (py)C(NH 2)NOH, and the anionic ligand present in the Mn(III) complex is 2,4-bis(2-pyridyl)-1,3,5-triazapentanedienate(-1), bptzpd-; py is the general abbreviation for the 2-pyridyl moiety.
<div> <p>Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules so vast, only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving towards the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the chemical space by comparing collaboration between human experimenters with an algorithm-based search, against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na<sub>6</sub>[Mo<sub>120</sub>Ce<sub>6</sub>O<sub>366</sub>H<sub>12</sub>(H<sub>2</sub>O)<sub>78</sub>]·200H<sub>2</sub>O (<b>1</b>). We show that the robot-human teams are able to increase the prediction accuracy to 75.6±1.8%, from 71.8±0.3% with the algorithm alone and 66.3±1.8% from only the human experimenters demonstrating that human-robot teams beat robots or humans working alone.</p> </div>
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