Genetic algorithms for computational materials discovery accelerated by machine learning

• Published in: npj computational materials 2019-04, Vol.5 (1), Article 46
• Main Authors: Jennings, Paul C., Lysgaard, Steen, Hummelshøj, Jens Strabo, Vegge, Tejs, Bligaard, Thomas
• Format: Article
• Language: English
• Subjects: 639/301/1034/1037; 639/638/563; 639/925/357/354; Algorithms; Artificial intelligence; Binary alloys; Catalysts; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Computer applications; Datasets; Density functional theory; Genetic algorithms; Learning algorithms; Machine learning; MATERIALS SCIENCE; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Nanoalloys; Nanoparticles; Search algorithms; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-019-0181-4

Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations.