Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Genetic programming-based learning of carbon interatomic potential for
  materials discovery   Efficient and accurate interatomic potential functions are critical to
computational study of materials while searching for structures with desired
properties. Traditionally, potential functions or energy landscapes are
designed by experts based on theoretical or heuristic knowledge. Here, we
propose a new approach to leverage strongly typed parallel genetic programming
(GP) for potential function discovery. We use a multi-objective evolutionary
algorithm with NSGA-III selection to optimize individual age, fitness, and
complexity through symbolic regression. With a DFT dataset of 863 unique carbon
allotrope configurations drawn from 858 carbon structures, the generated
potentials are able to predict total energies within $\pm 7.70$ eV at low
computational cost while generalizing well across multiple carbon structures.
Our code is open source and available at
\url{http://www.github.com/usccolumbia/mlpotential

18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
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
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess