Stacked Ensemble Learning For Range-separation Parameters

Ju, Cheng-Wei

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https://hdl.handle.net/2142/111246 Copy

Description

Title

Stacked Ensemble Learning For Range-separation Parameters

Author(s)

Ju, Cheng-Wei

Contributor(s)

• Lin, Zhou
• Kohn, Alexander W.
• Geva, Nadav
• French, Ethan

Issue Date

2021-06-23

Keyword(s)

Theory and Computation

Abstract

\begin{wrapfigure}{l}{0pt} \includegraphics[width=2.2in]{Pic_ISMS.eps} \end{wrapfigure} High-throughput quantum chemical calculations, especially those based on Kohn-Sham density functional theory (KS-DFT), have achieved tremendous success in materials discovery. However, due to the notorious self-interaction error, many popular exchange-correlation (XC) functionals suffer from catastrophic failures for molecules with delocalized electronic densities. As an effective but expensive solution to this problem, the optimally tuned range-separation hybrid functional utilizes a system-specific range-separation parameter ($\omega$). [T. Stein, L. Kronik, and R. Baer, $J.$ $Am.$ $Chem.$ $Soc$. $\textbf{2009}$, 131, 2818.] To accelerate the search for optimal $\omega$ and makes it practical for the high-throughput materials screening, we developed a stacked ensemble learning approach based on composite molecular descriptors, and assessed its accuracy and efficiency using the LRC-$\omega$PBE functional and a diverse database of over 4,000 molecules that are equally divided into the training and test sets. Compared with the traditional optimal tuning method, our stacked ensemble learning algorithm reached a mean absolute error (MAE) of 0.005 $a_0^{-1}$ in the values of $\omega$ while reducing the time cost by four orders of magnitude. In addition, the predictive power in essential properties such as fundamental and optimal band gaps were not compromised. Given sufficient diversity in the training set, stacked ensemble learning provides a promising alternative scheme to determine range-separation hybrid functional and eventually any system-specific XC functionals.

Publisher

International Symposium on Molecular Spectroscopy

Type of Resource

Text

Language

eng

Permalink

http://hdl.handle.net/2142/111246

DOI

10.15278/isms.2021.WE01

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