Deep-learning-based prediction of nanoparticle phase transitions during
in situ transmission electron microscopy
- URL: http://arxiv.org/abs/2205.11407v1
- Date: Mon, 23 May 2022 15:50:24 GMT
- Title: Deep-learning-based prediction of nanoparticle phase transitions during
in situ transmission electron microscopy
- Authors: Wenkai Fu, Steven R. Spurgeon, Chongmin Wang, Yuyan Shao, Wei Wang,
Amra Peles
- Abstract summary: We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous frames.
This capability provides insight into size dependent structural changes in Au nanoparticles under dynamic reaction condition.
It may be possible to anticipate the next steps of a chemical reaction for emerging automated experimentation platforms.
- Score: 3.613625739845355
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We develop the machine learning capability to predict a time sequence of
in-situ transmission electron microscopy (TEM) video frames based on the
combined long-short-term-memory (LSTM) algorithm and the features
de-entanglement method. We train deep learning models to predict a sequence of
future video frames based on the input of a sequence of previous frames. This
unique capability provides insight into size dependent structural changes in Au
nanoparticles under dynamic reaction condition using in-situ environmental TEM
data, informing models of morphological evolution and catalytic properties. The
model performance and achieved accuracy of predictions are desirable based on,
for scientific data characteristic, based on limited size of training data
sets. The model convergence and values for the loss function mean square error
show dependence on the training strategy, and structural similarity measure
between predicted structure images and ground truth reaches the value of about
0.7. This computed structural similarity is smaller than values obtained when
the deep learning architecture is trained using much larger benchmark data
sets, it is sufficient to show the structural transition of Au nanoparticles.
While performance parameters of our model applied to scientific data fall short
of those achieved for the non-scientific big data sets, we demonstrate model
ability to predict the evolution, even including the particle structural phase
transformation, of Au nano particles as catalyst for CO oxidation under the
chemical reaction conditions. Using this approach, it may be possible to
anticipate the next steps of a chemical reaction for emerging automated
experimentation platforms.
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