Computational Tools for the Prediction of Site- and Regioselectivity of Organic Reactions
Chemical Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
This
article
reviews
computational
tools
for
the
prediction
of
regio-
and
site-selectivity
organic
reactions.
It
spans
from
quantum
chemical
procedures
to
deep
learning
models
showcases
application
presented
tools.
Язык: Английский
Prediction and Explainable Analysis of Molecular Weight Distribution of Polystyrene Based on Machine Learning and SHAP
Macromolecular Reaction Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 25, 2025
Abstract
Molecular
weight
distribution
(MWD)
is
crucial
for
the
product
performance
of
polymers.
In
order
to
explore
how
process
conditions
affect
molecules
with
different
chain
lengths,
this
study
conducts
a
large
number
polystyrene
simulations
based
on
polymerization
kinetics
and
validates
them
through
pilot
plant
data
generate
reliable
dataset.
Machine
learning
methods
are
employed
predict
average
molecular
weights
conversion
rates.
Compared
extreme
gradient
boosting
(XGBoost)
support
vector
regression
(SVR),
fully
connected
neural
network
(FCNN)
shows
best
performance.
Furthermore,
an
improved
FCNN
model
feature
extractor
residual
structure
developed
MWD
accurately.
The
polymer
divided
into
10
bins
length,
influence
revealed
SHapley
Additive
exPlanations
(SHAP).
Notably,
reducing
feed
mass
fraction
ethylbenzene
increasing
charging
coefficient
second
pre‐polymerization
reactor
will
lead
increase
low
Raising
temperature
promote
decrease
in
proportion
small
molecule
polymers
ultra‐large
polymers,
thereby
narrowing
MWD.
addition,
specific
target
can
be
effectively
predicted
by
machine
learning.
Язык: Английский
Machine learning workflows beyond linear models in low-data regimes
Chemical Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
This
work
presents
automated
non-linear
workflows
for
studying
problems
in
low-data
regimes
alongside
traditional
linear
models.
Язык: Английский