{"id":"doi:10.1038/s41524-019-0181-4","kind":"article","title":"Genetic algorithms for computational materials discovery accelerated by machine learning","authors":["Paul C. Jennings","Steen Lysgaard","Jens S. Hummelshøj","Tejs Vegge","Thomas Bligaard"],"author_count":5,"year":2019,"published":"2019-04-10","venue":"npj Computational Materials","cited_by":269,"doi":"10.1038/s41524-019-0181-4","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41524-019-0181-4","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41524-019-0181-4","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2939893088","pdf_url":"https://www.nature.com/articles/s41524-019-0181-4.pdf","abstract":"A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Genetic algorithms for computational materials discovery accelerated by machine learning\" by Paul C. Jennings, Steen Lysgaard, Jens S. Hummelshøj et al., https://doi.org/10.1038/s41524-019-0181-4, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"ai-for-science","name":"AI for Science","domain_slug":"ai"}],"cited_in_dossiers":[]}