arXiv:2512.05976v3 Announce Type: replace-cross Abstract: Designing materials with controlled heat flow at the nano-scale is central to advances in microelectronics, thermoelectrics, and energy-conversion technologies. At these scales, phonon transport follows the Boltzmann Transport Equation (BTE), which captures non-diffusive (ballistic) effects but is too costly to solve repeatedly in inverse-design loops. Existing surrogate approaches trade speed for accuracy: fast macroscopic solvers can overestimate conductivities by hundreds of percent, while recent data-driven operator learners often r
The increasing computational demands for materials science and nanotechnology necessitate more efficient simulation methods right now, particularly with the advancements in AI for complex physics problems.
Improving the accuracy and speed of phonon transport simulations is crucial for designing next-generation microelectronics, thermoelectics, and energy conversion technologies, impacting efficiency and sustainability.
The ability to simulate phonon transport with enhanced speed and accuracy using AI will accelerate material discovery and optimization cycles, reducing reliance on costly experimental trial-and-error.
- · Materials science researchers
- · Semiconductor industry
- · Energy conversion technology developers
- · AI/ML model developers for scientific computing
- · Traditional high-cost simulation software providers
Faster and cheaper development of materials with tailored thermal properties.
Improved performance and energy efficiency in electronic devices and advanced thermoelectric systems.
Potential for new material paradigms enabling previously unachievable device functionalities and energy solutions.
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