{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stacked-ensemble-machine-learning-for-range","title":"Stacked Ensemble Machine Learning for Range-Separation Parameters","arxiv_id":null,"date":"2021-09-24","proceeding":"The Journal of Physical Chemistry Letters 2021 9","authors":["Cheng-Wei Ju","Ethan J. French","Nadav Geva","Alexander W. Kohn","and Zhou Lin"],"abstract":"Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the nonempirical but expensive optimally tuned range-separated hybrid (OT-RSH) functionals were developed. An OT-RSH transitions from a short-range (semi)local functional to a long-range Hartree−Fock exchange at a distance characterized by a molecule-specific range-separation parameter (ω). Herein, we propose a stacked ensemble machine learning model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1970 molecules with sufficient structural and functional diversity, and assessed its accuracy and efficiency using another 1956 molecules. Compared with nonempirical OT-ωPBE, ML-ωPBE reaches a mean absolute error of 0.00504a0−1 for optimal ω’s, reduces the computational cost by 2.66 orders of magnitude, and achieves comparable predictive power in optical properties.","url_abs":"https://pubs.acs.org/doi/full/10.1021/acs.jpclett.1c02506","url_pdf":"https://pubs.acs.org/doi/full/10.1021/acs.jpclett.1c02506","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stacked-ensemble-machine-learning-for-range","repo_url":"https://github.com/zhoulinumass/ML-wPBE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}