Crystal structure prediction using neural network potential and
  age-fitness Pareto genetic algorithm
Sadman Sadeed Omee Lai Wei Jianjun Hu
While crystal structure prediction (CSP) remains a longstanding challenge, we
introduce ParetoCSP, a novel algorithm for CSP, which combines a
multi-objective genetic algorithm (MOGA) with a neural network inter-atomic
potential (IAP) model to find energetically optimal crystal structures given
chemical compositions. We enhance the NSGA-III algorithm by incorporating the
genotypic age as an independent optimization criterion and employ the M3GNet
universal IAP to guide the GA search. Compared to GN-OA, a state-of-the-art
neural potential based CSP algorithm, ParetoCSP demonstrated significantly
better predictive capabilities, outperforming by a factor of $2.562$ across
$55$ diverse benchmark structures, as evaluated by seven performance metrics.
Trajectory analysis of the traversed structures of all algorithms shows that
ParetoCSP generated more valid structures than other algorithms, which helped
guide the GA to search more effectively for the optimal structures