diff --git a/source/_posts/abacus_ai.md b/source/_posts/abacus_ai.md index af56d51..6014dcd 100644 --- a/source/_posts/abacus_ai.md +++ b/source/_posts/abacus_ai.md @@ -6,7 +6,7 @@ categories: mathjax: true --- -Recently, doctoral student Sun Liang and researcher Chen Mohan from the Center for Applied Physics and Technology at Peking University implemented a machine learning-based kinetic energy density functional (multi-channel ML-based Physically-constrained Non-local KEDF, or CPN KEDF) in the domestically developed open-source density functional theory software ABACUS (Atomic-based Ab initio Computation at UStc). This functional employs a multi-channel architecture, extending the previously developed MPN KEDF (ML-based Physical-constrained Non-local KEDF) [1], which was designed for simple metallic systems, to semiconductors. The method achieved promising results in tests on ground-state energy and ground-state charge density, laying the groundwork for the development of machine learning-based kinetic energy density functionals with broader applicability. The article, titled “Multi-channel machine learning-based nonlocal kinetic energy density functional for semiconductors,” has been published in the journal Electronic Structure (DOI: 10.1088/2516-1075/ad8b8c) [2]. +Recently, doctoral student Liang Sun and researcher Mohan Chen from the Center for Applied Physics and Technology at Peking University implemented a machine learning-based kinetic energy density functional (multi-channel ML-based Physically-constrained Non-local KEDF, or CPN KEDF) in the domestically developed open-source density functional theory software ABACUS (Atomic-based Ab initio Computation at UStc). This functional employs a multi-channel architecture, extending the previously developed MPN KEDF (ML-based Physical-constrained Non-local KEDF) [1], which was designed for simple metallic systems, to semiconductors. The method achieved promising results in tests on ground-state energy and ground-state charge density, laying the groundwork for the development of machine learning-based kinetic energy density functionals with broader applicability. The article, titled “Multi-channel machine learning-based nonlocal kinetic energy density functional for semiconductors,” has been published in the journal Electronic Structure (DOI: 10.1088/2516-1075/ad8b8c) [2].