The Innovation Materials,
Journal Year:
2024,
Volume and Issue:
unknown, P. 100086 - 100086
Published: Jan. 1, 2024
<p>In
2005,
Science
magazine
listed
the
��nature
of
a
glassy
substance��
as
one
125
most
challenging
scientific
questions
century.
A
quantitative
understanding
time-temperature
transition
(TTT)
curve
for
critical
nucleation
amorphous
materials
is
crucial
to
answering
this
question.
Despite
extensive
efforts
over
past
70
years,
model
TTT
remains
elusive
due
lack
physical
properties
such
interfacial
energy
at
incubation
time
<i>t</i><sup>*</sup>
nucleation.
In
study,
relationship
between
viscosity
and
function
established
developed.
The
demonstrates
excellent
agreement
with
experimental
data
various
materials.
Most
importantly,
it
allows
accurate
definitive
determination
<i>T</i><sub>0</sub>,
true
minimum
crystallization
temperature
lower
end-point
curve,
well
below
which
liquid-to-solid
state
occurs.
This
offers
an
unambiguous
answer
nature
substances:
Above
liquid
constant
structure
relaxation;
solid
stable
structure.</p>
Processes,
Journal Year:
2024,
Volume and Issue:
12(7), P. 1541 - 1541
Published: July 22, 2024
With
the
advancement
of
manufacturing
industry,
performing
submerged
arc
welding
subject
to
varying
heat
inputs
has
become
essential.
However,
traditional
thermodynamic
models
are
insufficient
for
predicting
effect
input
on
elemental
transfer
behavior.
This
study
aims
develop
a
model
via
CALPHAD
technology
predict
influence
essential
elements
such
as
O,
Si,
and
Mn
when
typical
SiO2-bearing
fluxes
employed.
The
predicted
data
demonstrate
that
proposed
effectively
forecasts
changes
in
behavior
induced
by
inputs.
Furthermore,
discusses
factors
affecting
under
different
inputs,
supported
both
measured
compositions
data.
These
insights
may
provide
theoretical
technical
support
flux
design,
material
matching,
composition
prediction
various
conditions
processes
ACS Omega,
Journal Year:
2023,
Volume and Issue:
8(40), P. 37317 - 37328
Published: Sept. 27, 2023
The
μ
phase
is
a
type
of
hard
and
brittle
constituent
that
exists
in
high-temperature
alloys.
formation
energy
crucial
thermochemical
datum,
the
accurate
calculation
contributes
to
material
design
Traditional
first-principles
calculations
demand
significant
computational
time
resources.
In
this
study,
an
innovative
machine
learning
(ML)-based
approach
accurately
predict
proposed.
This
involves
utilization
six
algorithms
two
model
evaluation
methods
construct
ML
models.
Leveraging
comprehensive
data
set
containing
1036
binary
configurations
phase,
trained
using
10-fold
cross-validation
technique,
multilayer
perceptron
(MLP)
algorithm
achieves
mean
absolute
error
(MAE)
23.906
meV/atom.
To
validate
its
generalization
performance,
further
validated
on
900
ternary
configurations,
resulting
MAE
32.754
Compared
with
solely
traditional
calculations,
our
significantly
reduces
by
at
least
52%.
Moreover,
exhibits
exceptional
accuracy
predicting
lattice
parameters
phase.
values
for
The Innovation Materials,
Journal Year:
2024,
Volume and Issue:
unknown, P. 100086 - 100086
Published: Jan. 1, 2024
<p>In
2005,
Science
magazine
listed
the
��nature
of
a
glassy
substance��
as
one
125
most
challenging
scientific
questions
century.
A
quantitative
understanding
time-temperature
transition
(TTT)
curve
for
critical
nucleation
amorphous
materials
is
crucial
to
answering
this
question.
Despite
extensive
efforts
over
past
70
years,
model
TTT
remains
elusive
due
lack
physical
properties
such
interfacial
energy
at
incubation
time
<i>t</i><sup>*</sup>
nucleation.
In
study,
relationship
between
viscosity
and
function
established
developed.
The
demonstrates
excellent
agreement
with
experimental
data
various
materials.
Most
importantly,
it
allows
accurate
definitive
determination
<i>T</i><sub>0</sub>,
true
minimum
crystallization
temperature
lower
end-point
curve,
well
below
which
liquid-to-solid
state
occurs.
This
offers
an
unambiguous
answer
nature
substances:
Above
liquid
constant
structure
relaxation;
solid
stable
structure.</p>
