Galaxy formation and evolution critically depend on understanding the complex
photo-chemical processes that govern the evolution and thermodynamics of the
InterStellar Medium (ISM). Computationally, solving chemistry is among the most
heavy tasks in cosmological and astrophysical simulations. The evolution of
such non-equilibrium photo-chemical network relies on implicit, precise,
computationally costly, ordinary differential equations (ODE) solvers. Here, we
aim at substituting such procedural solvers with fast, pre-trained, emulators
based on neural operators. We emulate a non-equilibrium chemical network up to
H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism,
i.e. by splitting the ODE solver operator that maps the initial conditions and
time evolution into a tensor product of two neural networks. We use
$\texttt{KROME}$ to generate a training set spanning $-2\leq
\log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$,
$-6 \leq \log(n_i/n) < 0$, and by adopting an incident radiation field
$\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately
train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared
with the reference solutions obtained by $\texttt{KROME}$ for single zone
models, the typical precision obtained is of order $10^{-2}$, i.e. the $10
\times$ better with a training that is $40 \times$ less costly with respect to
previous emulators which however considered only a fixed $\mathbf{F}$. The
present model achieves a speed-up of a factor of $128 \times$ with respect to
stiff ODE solvers. Our neural emulator represents a significant leap forward in
the modeling of ISM chemistry, offering a good balance of precision,
versatility, and computational efficiency.

星系的形成和演化取决于对控制星际介质（ISM）演化和热力学的复杂光化学过程的理解。在宇宙学和天体物理模拟中，解决化学问题是最繁重的任务之一。本研究通过采用基于神经算子的快速预训练模拟器，模拟了非平衡化学网络直到 H$_2$ 的形成，并取得了较高的精度、高效率的能力，从而在 ISM 化学模型的建模方面取得了重大进展。