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Development of the Rapid Energy Gap (REG) AI Model for Instantaneous Determination of the HOMO-LUMO Gap

CSEF · 2023 Chemistry (Senior Division)

Isaac Wang

Overview

Molecular Orbital Theory is a method of modeling molecules through complex quantum mechanical calculations. This theoretical framework relies on the electron probability density of a region of space, also known as a molecular orbital. Of particular significance in Molecular Orbital Theory is the HOMO-LUMO Gap, which represents the energy difference between a molecule’s highest occupied molecular orbital (HOMO) and its lowest unoccupied molecular orbital (LUMO). The HOMO-LUMO Gap has a variety of real-world applications, from organic solar cells to drug design. When designing antiviral drugs, for instance, a smaller HOMO-LUMO Gap would indicate greater efficacy for the drug to inhibit the activity of the viral surface protein and thus prevent infection. However, it is difficult to calculate without the use of expensive and computationally intensive equipment due to the intricacy of current mathematical models. Thus, a streamlined Quantum Machine Learning (QML) approach is preferable. Through the Rapid Energy Gap (REG) AI model, the HOMO-LUMO Gap can be predicted with a satisfying degree of certainty. The model accesses the molecule’s molfile along with descriptors, including the molecular weight, LogP, Hydrogen Donors, and Hydrogen Acceptors. Reciprocal Net, a digital database of molecular structures and software tools, was used as data to train and validate REG. The true HOMO-LUMO gap was calculated through the PSI-4 open-source library. Findings indicate REG had a low mean absolute error of 0.003161 with only 16 epochs. By simplifying the calculations in Molecular Orbital Theory, REG has the potential to facilitate innovation in the quantum chemistry field.

Source coverage

This record comes from a published award list, not a complete project archive. Its abstract comes from CSEF's public project showcase as archived by the Internet Archive before judging (https://web.archive.org/web/20230401224130/https://ca-csef.zfairs.com/showcase/ShowcaseInfo?f=838e60b7-ea75-46e8-865c-fde4864244b3); the version presented may differ.

Awards (1)

  • Sponsored Award: Senior Division Chemistry Award

Competition history

  • CSEF 2023 Chemistry (Senior Division) · Entry S0603

Resources

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