arXiv:2509.15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. Machine learning offers a compelling pathway to accelerate the exploration, but existing models lack either interpretability or fidelity for elucidating the correlation between crystal geometry and property. Here, we report a three-dimensional periodic space sampling method that decomposes large nanoporous structures into local geometrical sites for combined property prediction and site-wise contri
This development leverages advanced AI techniques to address the long-standing challenge of designing complex materials, indicating a maturation of AI’s application in physical sciences.
Efficient design of nanoporous materials has broad implications for sustainable technologies, and this AI-driven approach significantly accelerates their discovery and optimization.
The design process for complex materials shifts from empirical trial-and-error to interpretable, AI-guided exploration, potentially speeding up R&D cycles and uncovering novel material properties.
- · Materials scientists
- · Chemicals industry
- · Sustainable technology sector
- · AI-driven R&D platforms
- · Traditional empirical materials design methods
Accelerated discovery of new nanoporous materials with tailored properties for various applications.
Improved efficiency and cost-effectiveness in areas like carbon capture, catalysis, and energy storage.
The development of entirely new sustainable technologies previously unattainable due to material design limitations.
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Read at arXiv cs.AI