热化学
密度泛函理论
系列(地层学)
统计物理学
轨道自由密度泛函理论
混合功能
幂级数
工作(物理)
边距(机器学习)
变量(数学)
功率(物理)
计算化学
数学
物理
应用数学
热力学
量子力学
化学
计算机科学
数学分析
生物
机器学习
古生物学
摘要
In a recent paper [A. D. Becke, J. Chem. Phys. 156, 214101 (2022)], we compared two Kohn–Sham density functionals based on physical modeling and theory with the best density-functional power series fits in the literature. With only a handful of physically motivated pre-factors, our functionals matched, and even slightly exceeded, the performance of the best power-series functionals on the general main group thermochemistry, kinetics, and noncovalent interactions (GMTKN55) chemical database of Goerigk et al. [Phys. Chem. Chem. Phys. 19, 32184 (2017)]. This begs the question: how much can their performance be improved by adding power-series terms of our own? We address this question in the present work. First, we describe a series expansion variable that we believe contains more local physics than any other variable considered to date. Then we undertake modest, one-dimensional fits to the GMTKN55 data with our theory-based functional corrected by power-series exchange and dynamical correlation terms. We settle on 12 power-series terms (plus six parent terms) and achieve the lowest GMTKN55 “WTMAD2” error yet reported, by a substantial margin, for a hybrid Kohn–Sham density functional. The new functional is called “B22plus.”
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