Accelerating Chemical Kinetics for Exoplanet Atmospheres using Neural Networks
Observations increasingly reveal the coupled radiative, chemical, and dynamical processes that shape exoplanet atmospheres. Interpreting these atmospheres requires models that can capture this complexity. However, multidimensional models remain fundamentally limited by computational cost, and answering key questions requires simulating the governing physical mechanisms at speeds classical methods cannot achieve. As a result, models often rely on simplifying approximations, such as equilibrium chemistry, even when those assumptions miss important effects. There is a pressing need for fast and accurate chemical kinetics solvers to model planetary atmospheres. Here we present a machine learn...
| Authors | Isaac Malsky, Xi Zhang, Tiffany Kataria et al. |
|---|---|
| Primary category | astro-ph.EP |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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