Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation has sparked interest in optimization-guided autonomous discovery and closed-loop experimentation. Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials. However, the experiment planning strategy that is most suitable to a scientific discovery task is a priori unknown while rigorous comparisons of different strategies are highly time and resource demanding. As optimization algorithms are typically benchmarked on low-dimensional synthetic functions, it is unclear how their performance would translate to noisy, higher-dimensional experimental tasks encountered in chemistry and materials science. We introduce Olympus, a software package that provides a consistent and easy-to-use framework for benchmarking optimization algorithms against realistic experiments emulated via probabilistic deep-learning models. Olympus includes a collection of experimentally derived benchmark sets from chemistry and materials science and a suite of experiment planning strategies that can be easily accessed via a user-friendly Python interface. Furthermore, Olympus facilitates the integration, testing, and sharing of custom algorithms and user-defined datasets. In brief, Olympus mitigates the barriers associated with benchmarking optimization algorithms on realistic experimental scenarios, promoting data sharing and the creation of a standard framework for evaluating the performance of experiment planning strategies.

Methylalumoxane (MAO), a perennially useful activator for olefin polymerization precatalysts, is famously intractable to structural elucidation, consisting as it does of a complex mixture of oligomers generated from hydrolysis of...

Hydrolysis of trimethylaluminum (Me3Al) in polar solvents can be monitored by electrospray ionization mass spectrometry (ESI‐MS) using the donor additive octamethyltrisiloxane [(Me3SiO)2SiMe2, OMTS]. Using hydrated salts, hydrolytic methylaluminoxane (h‐MAO) features different anion distributions, depending on the conditions of synthesis, and different activator contents as measured by NMR spectroscopy. Non‐hydrolytic MAO was prepared using trimethylboroxine. The properties of this material, which contains incorporated boron, differ significantly from h‐MAO. In the case of MAO prepared by direct hydrolysis, oligomeric anions are observed to rapidly form, and then more slowly evolve into a mixture dominated by an anion with m/z 1375 with formula [(MeAlO)16(Me3Al)6Me]−. Theoretical calculations predict that sheet structures with composition (MeAlO)n(Me3Al)m are favoured over other motifs for MAO in the size range suggested by the ESI‐MS experiments. A possible precursor to the m/z 1375 anion is a local minimum based on the free energy released upon hydrolysis of Me3Al.

<p>Methylalumoxane (MAO), a perennially useful activator for olefin polymerization precatalysts, is famously intractable to structural elucidation, consisting as it does of a complex mixture of oligomers generated from hydrolysis of pyrophoric trimethylaluminum (TMA). Electrospray ionization mass spectrometry (ESI-MS) is capable of studying those oligomers that become charged during the activation process. We’ve exploited that ability to probe the synthesis of MAO in real time, starting less than a minute after the mixing of H₂O and TMA and tracking the first half hour of reactivity. We find that the process does not involve an incremental build-up of oligomers; instead, oligomerization to species containing 12-15 aluminum atoms happens within a minute, with slower aggregation to higher molecular weight ions. The principal activated product of the benchtop synthesis is the same as that observed in industrial samples, namely [(MeAlO)₁₆(Me₃Al)₆Me]^–, and we have computationally located a new sheet structure for this ion 94 kJ mol^-1 lower in Gibbs energy than any previously calculated.</p>

<p>Methylalumoxane (MAO), a perennially useful activator for olefin polymerization precatalysts, is famously intractable to structural elucidation, consisting as it does of a complex mixture of oligomers generated from hydrolysis of pyrophoric trimethylaluminum (TMA). Electrospray ionization mass spectrometry (ESI-MS) is capable of studying those oligomers that become charged during the activation process. We’ve exploited that ability to probe the synthesis of MAO in real time, starting less than a minute after the mixing of H₂O and TMA and tracking the first half hour of reactivity. We find that the process does not involve an incremental build-up of oligomers; instead, oligomerization to species containing 12-15 aluminum atoms happens within a minute, with slower aggregation to higher molecular weight ions. The principal activated product of the benchtop synthesis is the same as that observed in industrial samples, namely [(MeAlO)₁₆(Me₃Al)₆Me]^–, and we have computationally located a new sheet structure for this ion 94 kJ mol^-1 lower in Gibbs energy than any previously calculated.</p>