Materials Genome Engineering Advances,
Год журнала:
2023,
Номер
1(2)
Опубликована: Ноя. 10, 2023
Abstract
Individual
phases
are
commonly
considered
as
the
building
blocks
of
materials.
However,
accurate
theoretical
prediction
properties
individual
remains
elusive.
The
top‐down
approach
by
decoding
genomic
from
experimental
observations
is
nonunique.
density
functional
theory
(DFT),
a
state‐of‐the‐art
solution
quantum
mechanics,
prescribes
existence
ground‐state
configuration
at
0
K
for
given
system.
It
self‐evident
that
alone
insufficient
to
describe
phase
finite
temperatures
symmetry‐breaking
non‐ground‐state
configurations
excited
statistically
above
K.
Our
multiscale
entropy
(recently
terms
Zentropy
theory)
postulates
composed
sum
each
weighted
its
probability
plus
configurational
among
all
configurations.
Consequently,
partition
function
in
statistical
mechanics
needs
be
evaluated
free
energy
rather
than
total
energy.
combination
and
represents
materials
can
used
quantitatively
predict
with
predicted
DFT
well
derived
phases.
Journal of Physics Condensed Matter,
Год журнала:
2024,
Номер
36(34), С. 343003 - 343003
Опубликована: Май 3, 2024
Abstract
Today’s
thermodynamics
is
largely
based
on
the
combined
law
for
equilibrium
systems
and
statistical
mechanics
derived
by
Gibbs
in
1873
1901,
respectively,
while
irreversible
nonequilibrium
resides
essentially
Onsager
Theorem
as
a
separate
branch
of
developed
1930s.
Between
them,
quantum
was
invented
quantitatively
solved
terms
density
functional
theory
(DFT)
1960s.
These
three
scientific
domains
operate
different
principles
are
very
much
separated
from
each
other.
In
analogy
to
parable
blind
men
elephant
articulated
Perdew,
they
individually
represent
portions
complex
system
thus
incomplete
themselves
alone,
resulting
lack
quantitative
agreement
between
their
predictions
experimental
observations.
Over
last
two
decades,
author’s
group
has
multiscale
entropy
approach
(recently
termed
zentropy
theory)
that
integrates
DFT-based
capable
accurately
predicting
free
energy
systems.
Furthermore,
combination
with
presented
Hillert,
author
cross
phenomena
beyond
phenomenological
Theorem.
The
jointly
provide
predictive
theories
electronic
any
observable
scales
reviewed
present
work.
The Journal of Chemical Physics,
Год журнала:
2024,
Номер
160(20)
Опубликована: Май 28, 2024
The
melting
temperature
is
important
for
materials
design
because
of
its
relationship
with
thermal
stability,
synthesis,
and
processing
conditions.
Current
empirical
computational
point
estimation
techniques
are
limited
in
scope,
feasibility,
or
interpretability.
We
report
the
development
a
machine
learning
methodology
predicting
temperatures
binary
ionic
solid
materials.
evaluated
different
machine-learning
models
trained
on
dataset
points
476
non-metallic
crystalline
compounds
using
embeddings
constructed
from
elemental
properties
density-functional
theory
calculations
as
model
inputs.
A
direct
supervised-learning
approach
yields
mean
absolute
error
around
180
K
but
suffers
low
find
that
fidelity
predictions
can
further
be
improved
by
introducing
an
additional
unsupervised-learning
step
first
classifies
before
melting-point
regression.
Not
only
does
this
two-step
exhibit
accuracy,
also
provides
level
interpretability
insights
into
feature
importance
types
depend
specific
atomic
bonding
inside
material.
Motivated
finding,
we
used
symbolic
to
interpretable
physical
temperature,
which
recovered
best-performing
features
both
prior
provided
Abstract
Advanced
ceramic
materials
and
devices
call
for
better
reliability
damage
tolerance.
In
addition
to
their
strong
bonding
nature,
there
are
examples
demonstrating
superior
mechanical
properties
of
nanostructure
ceramics,
such
as
damage‐tolerant
aerogels
that
can
withstand
high
deformation
without
cracking
local
plasticity
in
dense
nanocrystalline
ceramics.
The
recent
progresses
shall
be
reviewed
this
perspective
article.
Three
topics
including
highly
elastic
nano‐fibrous
aerogels,
load‐bearing
nanoceramics
with
improved
properties,
implementing
machine
learning‐assisted
simulations
toolbox
understanding
the
relationship
among
structure,
mechanisms,
microstructure‐properties
discussed.
It
is
hoped
perspectives
present
here
help
discovery,
synthesis,
processing
future
structural
insensitive
flaws
damages
service.
