Abstract

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528–4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. Here, the quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6–31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6–31+G(2df,p) and ωB97XD/6–311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantummore » chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening.« less

Authors:

Dandu, Naveen K.  ^[1];

• Search DOE PAGES for author "Dandu, Naveen K."
• Search DOE PAGES for ORCID "0000-0001-7122-8537"

Ward, Logan  ^[2];

• Search DOE PAGES for author "Ward, Logan"
• Search DOE PAGES for ORCID "0000-0002-1323-5939"

Assary, Rajeev S.  ^[2];

• Search DOE PAGES for author "Assary, Rajeev S."
• Search DOE PAGES for ORCID "0000-0002-9571-3307"

Redfern, Paul C. ^[2]; Curtiss, Larry A.  ^[2]

• Search DOE PAGES for author "Curtiss, Larry A."
• Search DOE PAGES for ORCID "0000-0001-8855-8006"

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+ Show Author Affiliations

1. Argonne National Laboratory (ANL), Argonne, IL (United States); Univ. of Illinois, Chicago, IL (United States)
2. Argonne National Laboratory (ANL), Argonne, IL (United States)

Publication Date:

Wed Jul 05 00:00:00 EDT 2023

Research Org.:

Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:

2007518

Grant/Contract Number:  

AC02-06CH11357

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory

Additional Journal Information:

Journal Volume: 127; Journal Issue: 28; Journal ID: ISSN 1089-5639

Publisher:

American Chemical Society

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; chemical calculations; density functional theory; energy; ionization; molecules