Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CCDCGAN: Inverse design of crystal structures   Autonomous materials discovery with desired properties is one of the ultimate
goals for modern materials science. Applying the deep learning techniques, we
have developed a generative model which can predict distinct stable crystal
structures by optimizing the formation energy in the latent space. It is
demonstrated that the optimization of physical properties can be integrated
into the generative model as on-top screening or backwards propagator, both
with their own advantages. Applying the generative models on the binary Bi-Se
system reveals that distinct crystal structures can be obtained covering the
whole composition range, and the phases on the convex hull can be reproduced
after the generated structures are fully relaxed to the equilibrium. The method
can be extended to multicomponent systems for multi-objective optimization,
which paves the way to achieve the inverse design of materials with optimal
properties.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom