Scaling atom-by-atom inverse design with nano-topology optimization and diffusion models
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arXiv
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| Natura: | Preprint |
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2026
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| _version_ | 1866914444720734208 |
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| author | Chen, Chun-Teh Lau, Denvid |
| author_facet | Chen, Chun-Teh Lau, Denvid |
| contents | The mechanical properties of metallic nanostructures are governed not only by topology but also by crystal symmetry and face-specific surface physics, which are typically absent from continuum topology optimization. We develop an atom-by-atom inverse design framework that combines Nano-Topology Optimization (Nano-TO) with conditional denoising diffusion probabilistic models. Nano-TO treats each atom as a discrete design variable and evaluates stiffness from the symmetric curvature of the total energy, removing residual surface-stress bias. A crystallography-aligned multi-shell sensitivity filter stabilizes the optimization and enables designs containing more than 6.5 x 10^5 atoms. Using aluminum nanocantilevers, we identify a surface-physics-driven topology selection rule: thickness-periodic beams favor brace-dominated trusses, whereas finite-thickness beams favor nearly closed walls that provide efficient shear paths and reduce surface penalties. At sufficiently small scales, these walls become mechanically unstable, and truss-like layouts reappear. In nanopillar studies, atomistic optimization outperforms continuum topology-optimized designs. Finally, conditional diffusion models trained on Nano-TO data generate diverse high-performance candidates near the optimization frontier. These results establish nanoscale inverse design as a coupled problem of topology and surface physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03276 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scaling atom-by-atom inverse design with nano-topology optimization and diffusion models Chen, Chun-Teh Lau, Denvid Applied Physics Mesoscale and Nanoscale Physics Materials Science Computational Physics The mechanical properties of metallic nanostructures are governed not only by topology but also by crystal symmetry and face-specific surface physics, which are typically absent from continuum topology optimization. We develop an atom-by-atom inverse design framework that combines Nano-Topology Optimization (Nano-TO) with conditional denoising diffusion probabilistic models. Nano-TO treats each atom as a discrete design variable and evaluates stiffness from the symmetric curvature of the total energy, removing residual surface-stress bias. A crystallography-aligned multi-shell sensitivity filter stabilizes the optimization and enables designs containing more than 6.5 x 10^5 atoms. Using aluminum nanocantilevers, we identify a surface-physics-driven topology selection rule: thickness-periodic beams favor brace-dominated trusses, whereas finite-thickness beams favor nearly closed walls that provide efficient shear paths and reduce surface penalties. At sufficiently small scales, these walls become mechanically unstable, and truss-like layouts reappear. In nanopillar studies, atomistic optimization outperforms continuum topology-optimized designs. Finally, conditional diffusion models trained on Nano-TO data generate diverse high-performance candidates near the optimization frontier. These results establish nanoscale inverse design as a coupled problem of topology and surface physics. |
| title | Scaling atom-by-atom inverse design with nano-topology optimization and diffusion models |
| topic | Applied Physics Mesoscale and Nanoscale Physics Materials Science Computational Physics |
| url | https://arxiv.org/abs/2604.03276 |