Understanding the
structure and properties of MgCl2/TiCl4 nanoclusters
is a key to uncovering the origin of Ziegler–Natta
catalysis. In particular, vibrational spectroscopy can sensitively
probe the morphology and active species of MgCl2/TiCl4. Here, we determined vibrational spectroscopic fingerprints
of 50MgCl2 and 50MgCl2/3TiCl4 which
were obtained by nonempirical structure determination based on an
evolutionary algorithm and DFT. The adsorption of CO, TiCl4, and Ti2Cl8 dimers was also modeled on each
of the coordinatively unsaturated Mg2+ sites available
for binding including so-called defect sites, which are likely present
at the surface of activated MgCl2 nanocrystals and plausible
sites for strong TiCl4 species adsorption. The outcomes
of thermodynamical and vibrational analysis were compared to results
on ideal surfaces of MgCl2. Vibrational analysis (IR and
Raman) on plausible models of TiCl4/ MgCl2 nanoclusters
revealed that IR response is useful for distinguishing between the
different ways of binding of TiCl4 on different sites of
adsorption, whereas Raman response provides a clear fingerprint of
supported TiCl4 species.
In the oxidative coupling of methane (OCM), the activation of methane and the suppression of deep oxidation are in a persistent trade-off relationship, and a catalyst design strategy that balances the activity and the selectivity is desired. In this study, we analyzed a random catalyst dataset for OCM that was earlier obtained by high-throughput experimentation, and extracted heuristics such as elements, supports, and their combinations related to methane activation at a low temper-ature and selective formation of C 2 compounds at a high temperature. The obtained heuristics were used for catalyst development. The most effective was the use of a mixed support between La 2 O 3 and BaO, which improved the lowtemperature activity, the high-temperature selectivity, as well as the maximum C 2 yield. It was considered that La 2 O 3 acted as a heater and helped low-temperature operation of BaO, which is highly selective but not active at a low temperature.
The combination of genetic algorithm-based global search
and local
geometry optimization enables nonempirical structure determination
for complex materials such as practical solid catalysts. However,
premature convergence in the genetic algorithm hinders the determination
of the global minimum for complicated molecular systems. Here, we
implemented a distributed genetic algorithm based on the migration
from a structure database for avoiding the premature convergence,
and thus we realized the structure determination for TiCl4-capped MgCl2 nanoplates with experimentally consistent
sizes. The obtained molecular models are featured with a realistic
size and nonideal surfaces, representing actual primary particles
of catalysts.
The determination of catalyst nanostructures with first-principles
accuracy using genetic algorithms (GA) is very demanding due to the
cubic scaling of the computational cost of density functional theory
(DFT) calculations. Here, we demonstrate, for the case of Ziegler–Natta
MgCl2/TiCl4 nanoplates, how this structure determination
can be accelerated by employing a high-dimensional neural network
potential (HDNNP) of essentially DFT accuracy. First, when building
HDNNPs for MgCl2/TiCl4 clusters with computationally
tractable sizes, we found that the structural diversity in the training
set is crucial for obtaining HDNNPs reliably describing the large
variety of structures generated by GA. The resulting HDNNPs dramatically
accelerated the structure determination while yielding results consistent
with DFT. Subsequently, we developed a multistep adaptive procedure
to construct a HDNNP for MgCl2/TiCl4 clusters
consistent in size and TiCl4 coverage with experiments
where prior DFT results were scarcely collected. The structure determination
and analyses underline the importance of system size and composition
in order to predict some experimentally known facts such as the surface
morphology and population of isospecific sites.
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