JOURNAL ARTICLE
ChatGPT-assisted visualization of atomic orbitals: Understanding symmetry, mixed state, and superposition.
Published In: Modern Physics Letters B, 2025, v. 39, n. 28. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Han, Huiping; Wu, Liang 3 of 3
Abstract
For undergraduate students newly introduced to quantum mechanics, solving simple Schrödinger equations is relatively straightforward. However, the more profound challenge lies in comprehending the underlying physical principles embedded in the solutions. During my academic experience, a recurring conceptual difficulty was understanding why only s orbitals, and not others like p orbitals, exhibit spherical symmetry. At first glance, this seems paradoxical, given that the potential energy function itself is spherically symmetric. Specifically, why do p orbitals adopt a dumbbell shape instead of a spherical one? For a hydrogen atom with an electron in the 2 p state, which specific 2 p orbital does the electron occupy, and how do the x, y, and z-axes in 2 p x , 2 p y , and 2 p z connect to the real world? Additionally, is the atom still spherically symmetric in such a state? These questions relate to core concepts of quantum mechanics concerning symmetry, mixed state, and superposition. This paper delves into these questions by investigating this specific case, utilizing the advanced visualization capabilities offered by ChatGPT. This paper underscores the importance of emerging AI tools in enhancing students' understanding of abstract principles. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Modern Physics Letters B. 2025/10, Vol. 39, Issue 28, p1
- Document Type:Article
- Subject Area:History
- Publication Date:2025
- ISSN:0217-9849
- DOI:10.1142/S0217984925501489
- Accession Number:186109376
- Copyright Statement:Copyright of Modern Physics Letters B is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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