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Electronegativity-Inspired Multidimensional and Complex Representations for Modeling Chemical Environments in
Youngjin An1, Soyeon Jeon1, Da Bean Han1
1Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
The Journal of Physical Chemistry Letters
|August 6, 2026
Summary
New multidimensional and complex-valued electronegativity descriptors capture complex chemical environments, improving materials discovery. These advanced representations enhance accuracy in modeling atomic interactions within materials.
Area of Science:
- Materials Science
- Computational Chemistry
- Data-Driven Discovery
Background:
- Data-driven materials discovery relies on accurate chemical bonding descriptors.
- Conventional scalar electronegativity fails to represent complex electronic environments in materials.
Purpose of the Study:
- To introduce novel multidimensional and complex-valued electronegativity descriptors.
- To enhance the description of local chemical environments in materials.
Main Methods:
- Developed multidimensional electronegativity-inspired vectors from Pauling's and Mulliken's definitions.
- Incorporated ionization energy and electron affinity into vector components.
- Utilized crystal graph convolutional neural networks (CGCNNs) and complex-valued variants for validation.
Main Results:
- Demonstrated improved accuracy in capturing atomic interactions using the new descriptors.
- Observed enhanced convergence behavior in materials modeling.
- Validated the effectiveness of multidimensional and complex-valued representations.
Conclusions:
- Multidimensional and complex-valued electronegativity representations effectively model chemical environments.
- These advanced descriptors offer a promising avenue for accelerating materials discovery.
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