Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative deep learning as a tool for inverse design of high-entropy
  refractory alloys   Generative deep learning is powering a wave of new innovations in materials
design. In this article, we discuss the basic operating principles of these
methods and their advantages over rational design through the lens of a case
study on refractory high-entropy alloys for ultra-high-temperature
applications. We present our computational infrastructure and workflow for the
inverse design of new alloys powered by these methods. Our preliminary results
show that generative models can learn complex relationships in order to
generate novelty on demand, making them a valuable tool for materials
informatics.

10
The categories of this paper are: q-bio
1
The categories of this paper are: cs
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg