Update abacus_ai.md

Author: yaniguan

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@@ -3,6 +3,7 @@ title: "What Can ABAUCS Do too? | Using AI to describe the kinetic energy of ele
 date: 2024-11-18
 categories:
 - ABACUS
+- 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].
@@ -89,4 +90,4 @@ Reference:
 [2] Sun L, Chen M. Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors[J]. Electronic Structure, 2024.
 [3] Wang L W, Teter M P. Kinetic-energy functional of the electron density[J]. Physical Review B, 1992, 45(23): 13196.
 [4] Wang Y A, Govind N, Carter E A. Orbital-free kinetic-energy density functionals with a density-dependent kerne[J]. Physical Review B, 1999, 60(24): 16350.
-[5] Huang C, Carter E A. Nonlocal orbital-free kinetic energy density functional for semiconductors[J]. Physical Review B, 2010, 81(4): 045206.
+[5] Huang C, Carter E A. Nonlocal orbital-free kinetic energy density functional for semiconductors[J]. Physical Review B, 2010, 81(4): 045206.