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  1. Home
  3. Guided Adaptive Diffusion: An Evolutionary Framework For Multimodal Atomistic Structure Prediction.
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  3. Guided Adaptive Diffusion: An Evolutionary Framework For Multimodal Atomistic Structure Prediction.

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Guided Adaptive Diffusion: An Evolutionary Framework for Multimodal Atomistic Structure Prediction.

Alexander Adel1, Jakub Szmitek1, Benedikt Hartl2,3

  • 1Institute of Materials Chemistry, TU Wien, Vienna 1060, Austria.

Journal of Chemical Information and Modeling
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an adaptive diffusion framework for atomistic structure prediction. The novel approach effectively navigates complex energy landscapes to find optimal atomic structures, improving search efficiency.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Artificial Intelligence

Background:

  • Atomistic structure prediction is crucial for understanding materials properties.
  • High-dimensional potential energy surfaces pose significant challenges for traditional search algorithms.
  • Existing methods often struggle with scalability as dimensionality increases.

Purpose of the Study:

  • To develop a novel adaptive diffusion framework for efficient atomistic structure prediction.
  • To enhance the search for global and local minima on complex potential energy surfaces.
  • To overcome limitations of traditional algorithms in high-dimensional spaces.

Main Methods:

  • Reinterpreting neural network-based denoising as an evolutionary search mechanism.
  • Incorporating geometric constraints for physics-informed sampling.
  • Employing a memetic approach combining diffusion models with gradient-based relaxation.

    • Main Results:

    • Demonstrated ability to locate global and low-energy local minima for Lennard-Jones and gold clusters.
    • Framework remains effective on high-dimensional potential energy surfaces.
    • Maintained population diversity and search efficiency during optimization.

    Conclusions:

    • The adaptive diffusion framework offers a powerful new tool for atomistic structure prediction.
    • The approach successfully handles complex, multimodal potential energy surfaces.
    • This method represents a significant advancement in computational materials discovery.