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luMOD: A Lightweight Universal Midrange Atomic Orientational Descriptor.

Yanhao Deng1,2, Tianle Jiang1, Zeyu Deng2

  • 1University of Michigan─Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, 800 Dongchuan Rd., Minhang District, Shanghai 200240, P. R. China.

Journal of Chemical Theory and Computation
|November 5, 2025
PubMed
Summary
This summary is machine-generated.

We developed luMOD, a lightweight universal Midrange atomic Orientational Descriptor with only 24 features. This compact descriptor efficiently describes complex atomic environments, accelerating machine learning for materials discovery.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Extracting structural information from atomistic simulations is vital for machine learning in materials science.
  • Current descriptors often face high dimensionality and limited flexibility, hindering efficient analysis.

Purpose of the Study:

  • To develop a novel, compact, and versatile atomic descriptor for enhanced materials exploration.
  • To improve the efficiency and scalability of structural descriptors in machine learning applications.

Main Methods:

  • Development of luMOD (lightweight universal Midrange atomic Orientational Descriptor), a 24-feature descriptor.
  • Integration of bond orientational order parameters and neighbor species distributions.
  • Testing across diverse single- and multispecies systems, including perovskites and olivines.

Main Results:

  • luMOD efficiently describes heterogeneous atomic environments within multiple nearest-neighbor layers.
  • Demonstrated strong versatility in both single- and multispecies systems, including complex interfaces.
  • Reduced machine learning model training time, especially for multispecies systems.

Conclusions:

  • luMOD offers a promising, efficient, and scalable solution for structural description in atomistic simulations.
  • This development accelerates data-driven materials discovery and design.
  • The compact nature of luMOD enhances the practicality of machine learning in materials science.