Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accelerated design of Fe-based soft magnetic materials using machine
  learning and stochastic optimization   Machine learning was utilized to efficiently boost the development of soft
magnetic materials. The design process includes building a database composed of
published experimental results, applying machine learning methods on the
database, identifying the trends of magnetic properties in soft magnetic
materials, and accelerating the design of next-generation soft magnetic
nanocrystalline materials through the use of numerical optimization. Machine
learning regression models were trained to predict magnetic saturation ($B_S$),
coercivity ($H_C$) and magnetostriction ($\lambda$), with a stochastic
optimization framework being used to further optimize the corresponding
magnetic properties. To verify the feasibility of the machine learning model,
several optimized soft magnetic materials -- specified in terms of compositions
and thermomechanical treatments -- have been predicted and then prepared and
tested, showing good agreement between predictions and experiments, proving the
reliability of the designed model. Two rounds of optimization-testing
iterations were conducted to search for better properties.

21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th