Kim, Moonseop
DATA-DRIVEN MULTISCALE PREDICTION OF MATERIAL PROPERTIES USING MACHINE LEARNING ALGORITHMS
<div>
<div>
<div>
<p>The objective of this study is that combination of molecular dynamics (MD) simulations
and machine learning to complement each other. In this study, four steps are conducted.
</p>
<p>First is based on the empirical potentials development in silicon nanowires for theory parts of
molecular dynamics. Many-body empirical potentials have been developed for the last three
decades, and with the advance of supercomputers, these potentials are expected to be even more
useful for the next three decades. Atomistic calculations using empirical potentials can be
particularly useful in understanding the structural aspects of Si or Si-H systems, however, existing
empirical potentials have many errors of parameters. We propose a novel technique to understand
and construct interatomic potentials with an emphasis on parameter fitting, in which the
relationship between material properties and potential parameters is explained. The input database
has been obtained from density functional theory (DFT) calculations with the Vienna ab initio
simulation package (VASP) using the projector augmented-wave method within the generalized
gradient approximation. The DFT data are used in the fitting process to guarantee the compatibility
within the context of multiscale modeling.
</p>
<p>Second, application part of MD simulations, enhancement of mechanical properties was
focused in this research by using MEAM potentials. For instance, Youngâ€™s modulus, ultimate
tensile strength, true strain, true stress and stress-strain relationship were calculated for nanosized
Cu-precipitates using quenching & partitioning (Q&P) processing and nanosized Fe3C
strengthened ultrafine-grained (UFG) ferritic steel. In the stress-strain relationship, the structure
of simulation is defined using the constant total number of particles, constant-energy, constant-volume ensemble (NVE) is pulled in the y-direction, or perpendicular to the boundary interface,
to increase strain. The strain in increased for a specified number of times in a loop and the stress
is calculated at each point before the simulation loops.</p></div></div>
</div>
<div>
<div>
<div>
<p>Third, based on the MD simulations, machine learning and the peridynamics are applied to
prediction of disk damage patterns. The peridynamics is the nonlocal extension of classical
continuum mechanics and same as MD model. Especially, FEM is based on the partial differential
equations, however, partial derivatives do not exist on crack and damage surfaces. To complement
this problem, the peridynamics was used which is based on the integral equations and overcome
deficiencies in the modeling of deformation discontinuities. In this study, the forward problem (i),
if we have images of damage and crack, crack patterns are predicted by using trained data
compared to true solutions which are hit by changing the x and y hitting coordinates on the disk.
The inverse problem (ii), if we have images of damage and crack, the corresponding hitting
location, indenter velocity and indenter size are predicted by using trained data. Furthermore, we
did the regression analysis for the images of the crack patterns with Neural processes to predict
the crack patterns. In the regression problem, by representing the results of the variance according
to the epochs, it can be confirmed that the result of the variance is decreased by increasing the
epoch through the neural processes. Therefore, the result of the training gradually improves, and
the ranges of the variance are expressed as 0 to 0.035. The most critical point of this study is that
the neural processes makes an accurate prediction even if the information of the training data is
missing or not enough. The results show that if the context points are set to 10, 100, 300, and 784,
the training information is deliberately omitted such as context points of 10, 100 and 300, and the
predictions are different when context points are significantly lower. However, when comparing
the results of context points 100 and 784, the predicted results appear to be very similar to each
other because of the Gaussian processes in the neural processes. Therefore, if the training data is
trained through the Neural processes, the missing information of training data can be supplemented
to predict the results.
</p>
<p>Finally, we predicted the data by applying various data using deep learning as well as MD
simulation data. This study applied the deep learning to Cryo-EM images and Line Trip (LT) data
with power systems. In this study, deep learning method was applied to reduce the effort of
selection of high-quality particles. This study proposes a learning frame structure using deep
learning and aims at freeing passively selecting high quality particles as the ultimate goal. For
predicting the line trip data and bad data detection, we choose to analyze the frequency signal
because suddenly the frequency changes in the power system due to events such as generator trip,
line trip or load shedding in large power systems.
</p>
</div>
</div>
</div>
machine learning-based;Molecular Dynamics
2019-10-16
https://hammer.figshare.com/articles/thesis/DATA-DRIVEN_MULTISCALE_PREDICTION_OF_MATERIAL_PROPERTIES_USING_MACHINE_LEARNING_ALGORITHMS/9768107

10.25394/PGS.9768107.v1