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Artificial Intelligence for Quantum Sciences
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Artificial Intelligence for Quantum Sciences

A special issue of Atoms (ISSN 2218-2004).

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 2369

Special Issue Editor

Dr. Mehdi Ayouz

E-Mail Website
Guest Editor
CentraleSupélec, Université Paris Saclay, F-91190 Gif-sur-Yvette, France
Interests: modeling fundamental elementary processes involving photons, electrons, atoms, and molecules for applications ranging from engineering to plasma; use of novel tools such as machine learning in quantum sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen a sudden surge in the use of artificial intelligence (AI) methods in a plethora of fundamental sciences and engineering applications. Advances in this booming field have impacted the quantum sciences, yielding a rapid increase in the interest and confidence of the scientific and educational communities in AI-driven methods. One of the famous potential utilizations of AI in quantum sciences is undoubtedly quantum artificial intelligence, which could bring about a new computing revolution. Papers in this Special Issue provide insight into the current utilizations of AI in quantum sciences with examples of studies dealing with AI-driven methods for solving quantum problems.

This Special Issue will take the form of two main topics covering studies in research and education. Papers from both communities are welcome.

I look forward to receiving your submissions for the production of this Special Issue.

Dr. Mehdi Ayouz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atoms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum sciences
  • quantum technologies
  • artificial intelligence
  • machine learning
  • deep learning
  • research and education

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Published Papers (2 papers)

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Research

16 pages, 1425 KB  
Article
Combining Physics and Machine Learning: Hybrid Models for Predicting Interatomic Potentials
by Kaoutar El Haloui, Nicolas Thome and Nicolas Sisourat
Atoms 2025, 13(11), 89; https://doi.org/10.3390/atoms13110089 - 10 Nov 2025
Viewed by 936
Abstract
Constructing accurate Potential Energy Surfaces (PES) is a central task in molecular modeling, as it determines the forces governing nuclear motion and enables reliable quantum dynamics simulations. While ab initio methods can provide accurate PES, they are computationally prohibitive for extensive applications. Alternatively, [...] Read more.
Constructing accurate Potential Energy Surfaces (PES) is a central task in molecular modeling, as it determines the forces governing nuclear motion and enables reliable quantum dynamics simulations. While ab initio methods can provide accurate PES, they are computationally prohibitive for extensive applications. Alternatively, analytical physics-based models such as the Morse potential offer efficient solutions but are limited by their rigidity and poor generalization to excited states. In recent years, neural networks have emerged as powerful tools for determining PES, due to their universal function approximation capabilities, but they require large training datasets. In this work, we investigate hybrid-residual modeling approaches that combine physics-based potentials with neural network corrections, aiming to leverage both physical priors and data adaptability. Specifically, we compare three hybrid models—APHYNITY, Sequential Phy-ML, and PhysiNet—in their ability to reconstruct the potential energy curve of the ground and first excited states of the hydrogen molecule. Each model integrates a simplified physical representation with a neural component that learns the discrepancies from accurate reference data. Our findings reveal that hybrid models significantly outperform both standalone neural networks and pure physics-based models, especially in low-data regimes. Notably, APHYNITY and Sequential Phy-ML exhibit better generalization and maintain accurate estimation of physical parameters, underscoring the benefits of explicit physics incorporation. Full article
(This article belongs to the Special Issue Artificial Intelligence for Quantum Sciences)
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24 pages, 1467 KB  
Article
Introducing Machine Learning in Teaching Quantum Mechanics
by M. K. Pawelkiewicz, Filippo Gatti, Didier Clouteau, Viatcheslav Kokoouline and Mehdi Adrien Ayouz
Atoms 2025, 13(7), 66; https://doi.org/10.3390/atoms13070066 - 8 Jul 2025
Viewed by 959
Abstract
In this article, we describe an approach to teaching introductory quantum mechanics and machine learning techniques. This approach combines several key concepts from both fields. Specifically, it demonstrates solving the Schrödinger equation using the discrete-variable representation (DVR) technique, as well as the architecture [...] Read more.
In this article, we describe an approach to teaching introductory quantum mechanics and machine learning techniques. This approach combines several key concepts from both fields. Specifically, it demonstrates solving the Schrödinger equation using the discrete-variable representation (DVR) technique, as well as the architecture and training of neural network models. To illustrate this approach, a Python-based Jupyter notebook is developed. This notebook can be used for self-learning or for learning with an instructor. Furthermore, it can serve as a toolbox for demonstrating individual concepts in quantum mechanics and machine learning and for conducting small research projects in these areas. Full article
(This article belongs to the Special Issue Artificial Intelligence for Quantum Sciences)
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