Chemistry and Chemical Networks

The chemistry capabilities in DESPOTIC are located in the despotic.chemistry module.

Operations on Chemical Networks

The central object in a chemical calculation is a chemical network, which consists of a set of chemical species and a set of reactions that change the concentrations of each of them. Formally, a network is defined by a set of \(N\) species with abundances \(\mathbf{x}\) (defined as number density of each species per H nucleus), and a function \(d\mathbf{x}/dt = \mathbf{f}(\mathbf{x}, \mathbf{p})\) that gives the time rate of change of the abundances. The vector \(\mathbf{p}\) specifies a set of parameters on which the reaction rates depend, and it may include quantities such as the ambient radiation field (for photoreactions), the density, the temperature, etc. The numerical implementation of chemical networks is described in Defining New Chemical Networks: the chemNetwork Class. Given any chemical network, DESPOTIC is capable of two operations:

  • Given an initial set of abundances at \(\mathbf{x}(t_0)\) at time \(t_0\), compute the abundances at some later time \(t_1\). This is simply a matter of numerically integrating the ordinary differential equation \(d\mathbf{x}/dt = \mathbf{f}(\mathbf{x},\mathbf{p})\) from \(t_0\) to \(t_1\), where \(\mathbf{f}\) is a known function. In DESPOTIC, this capability is implemented by the routine chemEvol in the despotic.chemistry.chemEvol module. The cloud class contains a wrapper around this routine, which allows it to be called to operate on a specific instance of cloud or zonedcloud.
  • Find an equilibrium set of abundances \(\mathbf{x}_{\mathrm{eq}}\) such that \(d\mathbf{x}/dt = \mathbf{f}(\mathbf{x}_{\mathrm{eq}}, \mathbf{p}) = 0\). Note that these is in general no guarantee that such an equilibrium exists, or that it is unique, and there are no general techniques for identifying such equilibria for arbitrary vector functions \(\mathbf{f}\). DESPOTIC handles this problem by explicitly integrating the ODE \(d\mathbf{x}/dt = \mathbf{f}(\mathbf{x},\mathbf{p})\) until \(\mathbf{x}\) reaches constant values (within some tolerance) or until a specified maximum time is reached. In DESPOTIC, this capability is implemented by the routine setChemEq in the despotic.chemistry.setChemEq module. The cloud and zonedcloud classed contains a wrapper around this routine, which allows it to be called to operate on a specific instance of cloud or zonedcloud.

Predefined Chemical Networks

DESPOTIC ships with two predefined chemical networks, described below.


The NL99 network implements the reduced C-O network introduced by Nelson & Langer (1999, Astrophysical Journal, 524, 923; hereafter NL99). Readers are refereed to that paper for a full description of the network and the physical approximations on which it relies. To summarize briefly here, the network is intended to capture the chemistry of carbon and oxygen as it occurs at moderate densities and low temperatures in \(\mathrm{H}_2\) dominated clouds. It includes the species C, \(\mathrm{C}^+\), CHx, CO, \(\mathrm{HCO}^+\), \(\mathrm{H}^+_3\), \(\mathrm{He}^+\), O, OHx, M, and \(\mathrm{M}^+\). Several of these are “super-species” that agglomerate several distinct species with similar reaction rates and pathways, including CHx (where \(x = 1-4\)), OHx (where \(x = 1-2\)), and M and \(\mathrm{M}^+\) (which are stand-ins for metals such as iron and nickel). The network involves two-body reactions among these species, as well as photochemical reactions induces by UV from the ISRF and reactions initiated by cosmic ray ionizations. In addition to the initial abundances of the various species, the network depends on the ISRF, the ionization rate, and the total abundances of C and O nuclei.

In implementing the NL99 network in DESPOTIC there are a three design choices to be made. First, photochemical and ionization reactions depend on the UV radiation field strength and the ionization rate. When performing computations on a cloud, DESPOTIC takes these from the parameters chi and ionRate that are part of the radiation class attached to the cloud.

Second, photochemical reactions depend on the amount of shielding against the ISRF provided by dust, and, in the case of the reaction \(\mathrm{CO}\rightarrow\mathrm{C}+\mathrm{O}\), line shielding by CO and \(\mathrm{H}_2\). Following its usual approximation for implementing such shielding in a one-zone model, DESPOTIC takes the relevant column density to be \(N_{\mathrm{H}}/2\), where \(N_\mathrm{H}\) is the column density colDen of the cloud, so that the typical amount of shielding is assumed to correspond to half the area-averaged column density. For the dust shielding, NL99 express the shielding in terms of the V-band extinction \(A_V\); unless instructed otherwise, DESPOTIC computes this via

\[A_V = 0.4 \sigma_{\mathrm{PE}}(N_{\mathrm{H}}/2).\]

This ratio of V-band to 100 nm extinction is intermediate between the values expected for Milky Way and SMC dust opacity curves, as discussed in Krumholz, Leroy, & McKee (2011, Astrophysical Journal, 731, 25). However, the user may override this choice. For line shielding, DESPOTIC computes the \(\mathrm{H}_2\) and CO column densities

\[ \begin{align}\begin{aligned}N_{\mathrm{H}_2} = x_{\mathrm{H}_2} N_{\mathrm{H}}/2\\N_\mathrm{CO} = x_\mathrm{CO} N_\mathrm{H} /2,\end{aligned}\end{align} \]

which amounts to assuming that the CO and \(\mathrm{H}_2\) are uniformly distributed. Note that the NL99 network explicitly assumes \(x_{\mathrm{H}_2} = 0.5\), as no reactions involving atomic H are included – see NL99_GC for a network that does include hydrogen chemistry. These column densities are then used to find a shielding factor by interpolating the tabulated values of van Dishoeck & Black (1988, Astrophysical Journal, 334, 771).

The third choice is how to map between the species included in the chemistry network and the actual emitting species that are required to compute line emission, cooling, etc. This is non-trivial both because the chemical network includes super-species, because the chemical network does not distinguish between ortho- and para- sub-species while the rest of DESPOTIC does, and because the network does not distinguish between different isotopomers of the same species, while the rest of DESPOTIC does. This does not create problems in mapping from cloud emitter abundances to the chemical network, since the abundances can simply be summed, but it does create a question about how to map output chemical abundances computed by the network into the abundances of emitters that can be operated on by the remainder of DESPOTIC. In order to handle this issue, DESPOTIC makes the following assumptions:

  1. OHx is divided evenly between OH and \(\mathrm{OH}_2\)
  2. The ratio of ortho- to para- for all species is the same as that of \(\mathrm{H}_2\)
  3. The abundances ratios of all isotopomers of a species remain fixed as reactions occur, so, for example, the final abundance ratio of \(\mathrm{C}^{18}\mathrm{O}\) to \(\mathrm{C}^{16}\mathrm{O}\) as computed by the chemical network is always the same as the initial one.


The NL99_GC network is an extension of the NL99 network to handle hydrogen chemistry as well as carbon and oxygen chemistry, following the recipe described in Glover & Clark (2012, MNRAS, 421, 116). This network effectively uses NL99 for carbon and oxygen (with some updates to the rate coefficients) and the network of Glover & Mac Low (2007, ApJS, 169, 239) for the hydrogen chemistry. Self-shielding of hydrogen is handled via the approximate analytic shielding function of Draine & Bertoldi (1996, ApJ, 468, 269). Effective column densities for shielding are computed as in NL99. Calculations of hydrogen chemistry are assumed to leave the ratio of ortho- to para- \(\mathrm{H}_2\) unchanged from the initial value, or set it to 0.25 if the initial value is undefined (e.g., because the calculation started with a composition that was all H and no \(\mathrm{H}_2\)). All other assumptions are completely analogous to NL99.

Defining New Chemical Networks: the chemNetwork Class

DESPOTIC implements chemical networks through the abstract base class chemNetwork, which is defined by module despotic.chemistry.chemNetwork – see Full Documentation of All DESPOTIC Classes and Functions for full details. This class defines the required elements that all chemistry networks must contain; users who wish to implement their own chemistry networks must derive them from this class, and must override the class methods listed below. Users are encouraged to examine the two Predefined Chemical Networks for examples of how to derive a chemical network class from chemNetwork.

For any class cn derived from chemNetwork, the user is required to define the following non-callable traits:

  • cn.specList: a list of strings that describes the chemical species included in the network. The names in cn.specList can be arbitrary, and are not used for any purpose other than providing human-readable labels on outputs. However, it is often convenient to match the names to the names of emitters, as this makes it convenient to add the emitters back to the cloud later.
  • cn.x: a numpy array of rank 1, with each element specifying the abundance of a particular species in the network. The number of elements in the array must match the length of cn.specList. As with all abundances in DESPOTIC, abundances must be specified relative to H nuclei, so that if, for example, x[3] is 0.1, this means that there is 1 particle of species 3 per 10 H nuclei.
  • an instance of the cloud class to which the chemical network is attached. This can be None, as chemical networks can be free-standing at not attached to specified instances of cloud. However, much of the functionality of chemical networks is based around integration with the cloud class.

In addition, a class cn derived from chemNetwork must define the following callable attributes:

  • cn.__init__(self, cloud=None, info=None): this is the initialization method. It must accept two keyword arguments. The first, cloud, is an instanced of the cloud class to which this chemical network will be attached. This routine should set equal to the input cloud instance, and it may also extract information from the input cloud instances in order to initialize cn.x or any other required quantities. The second keyword argument, info, is a dict containing any additional initialization parameters that the chemical network can be or must be passed upon instantiation.
  • cn.dxdt(self, xin, time): this is a method that computes the time derivative of the abundances. Given an input numpy array of abundances xin (which is the same shape as cn.x) and a time time, it must return a numpy array giving the time derivative of all abundances in units of \(\mathrm{s}^{-1}.\)
  • cn.applyAbundances(self, addEmitters=False): this is a method to take the abundances stored in the chemical network and use them to update the abundances of the corresponding emitters in the cloud instance associated with this chemical network. This method is called at the end of every chemical calculation, and is responsible for copying information from the chemical network back into the cloud. The optional Boolean argument addEmitters, if True, specifies that the method should attempt to not only alter the abundances of any emitters associated with the cloud, it should also attempt to add new emitters that are included in the chemical network, and whose abundances are to be determined from it. It is up to the user whether or not to honor this request and implement this behavior. Failure to do so will not prevent the chemical network from operating, but should at least be warned so that other users are not surprised.

Finally, the following is an optional attribute of the derived class cn:

  • cn.abundances: this is a property that returns the abundance information defined in cn.x as a object of class abundanceDict. This class is a utility class that provides an interface to the chemical abundances in a network that operates like a Python dict. The property abundances is defined in the base chemNetwork class, so it is available by inheritance regardless of whether the user defines cn.abundances. However, the user may find it convenient to override chemNetwork.abundances to provide more information, e.g., to provide abundances of species that are not explicitly evolved in the network, and are instead derived via conservation relations. Both the NL99 and NL99_GC classes do this.

Once a chemical network class that defines the above methods has been defined, that class can be passed as an argument associated with the network keyword to the cloud.setChemEq and cloud.chemEvol methods (and their equivalents in zonedcloud), and these methods will automatically perform chemical calculations using the input network.

In setting up chemical networks, it often convenient to make use of the Helper Modules for Chemical Networks that are provided.

Helper Modules for Chemical Networks

The modules below, implemented in despotic.chemistry, are intended as helpers for defining and working with chemical networks.


The abundanceDict class provides a dict-like interface to numpy arrays containing species abundances. The motivation for its existence is that it is desirable to keep lists of chemical species abundances in a numpy array for speed of computation, but for humans it is much more convenient to be able to query and print chemical species by name, with a dict-like interface. The abundanceDict class overlays a dict-like structure on a numpy array, making it possible to combine the speed of a numpy array with the convenience of a dict.

Usage is simple. To define an abundanceDict one simply provides a set of chemical species names and a numpy array containing abundances of those species:

from despotic.chemistry import abundanceDict

# specList = list of strings containing species names
# x = numpy array containing species abundances
abd = abundanceDict(specList, x)

Once created, an abundanceDict can be handled much like a dict, in which the species names in specList are the keys:

# Print abundance of the species CO
print abd.abundances["CO"]

# Set the C+ abundance to 1e-10
abd["C+"] = 1e-10

These operations will alter the underlying numpy array appropriately; the array may also be accessed directly, as abundanceDict.x. However, the performance penalty for referring to objects by name rather than by index in abundanceDict.x is negligibly small in almost all cases, so it is recommended to use keys, as this makes for much more human-readable code.

Mathematical operations on the abundances will pass through and be applied to the underlying numpy array:

# Double the abundance of every species
abd = 2*abd

# Add the abundances of two different abundanceDict's, abd1 and abd2
abd3 = abd1 + abd2

Finally, abundanceDict instances differ from regular dict objects in that, once they are created, their species lists are immutable; species abundances can be changed, but species cannot be added or removed.

For full details on abundanceDict, see Full Documentation of All DESPOTIC Classes and Functions.


The reactions module provides a generic way to describe chemical reactions and compute their rates. It contains one basic class and several specialized derived classes to compute particualr types of reactions. The goal of all the classes in reactions is to allow users to describe the reactions in a chemical network in human-readable forms, but then compute their rates using efficient numpy operations at high speed.


The most general class in reactions is called reaction_matrix. To instantiate it, one provides a list of species and a list of reactions between them:

from despotic.chemistry.reactions import reaction_matrix
rm = reaction_matrix(specList, reactions)

Here specList is a list of strings giving the names of the chemical species, while reactions is a list of dict’s, one dict per reaction, describing each reaction. Each dict in reactions contains two keys: reactions["spec"] gives the list of species involved in the reaction, and reactions["stoich"] gives the corresponding stoichiometric factors, with reactants on the left hand side having negative factors (indicating that they are destroyed by the reaction) and those on the right having positive factors (indicating that they are created). Thus for example to include the reactions

\[\begin{split}\mathrm{C} + \mathrm{O} & \rightarrow \mathrm{CO} \\ \mathrm{H} + \mathrm{H} & \rightarrow \mathrm{H}_2\end{split}\]

in a network, one could define the reactions as:

reactions \
   = [ { "spec" : ["C", "O", "CO"], "stoich" : [-1, -1, 1] },
       { "spec" : ["H", "H2"], "stoich" : [-2, 1] } ]

Once a reaction_matrix has been defined, one can compute the rates of change of all species using the method reaction_matrix.dxdt:

dxdt = rm.dxdt(x, n, ratecoef)

Here x is a numpy array giving the instantaneous abundances of all species, n is the number density of H nuclei, and ratecoeff is a numpy array giving the rate coefficient for all reactions. The quantity returned is the instantaneous rate of change of all abundances x. The density-dependence of the reaction rates implied by the stoichiometric factors in the reaction list is computed automatically.


The cr_reactions class is a specialized class derived from reaction_matrix that is intended to handle cosmic ray-induced reactions – those where the rate is proportional to the cosmic ray ionization rate.

Instantiation of a cr_reactions object takes the same two arguments as reaction_matrix, but reactions, the list of reactions to be passed, is altered in that each reaction takes an additional key, rate, that gives the proportionality between the reaction rate and the cosmic ray ionization rate. Thus for example if we wished to include the reactions

\[\begin{split}\mathrm{CR} + \mathrm{H} & \rightarrow \mathrm{H}^+ + \mathrm{e}^- \\ \mathrm{CR} + \mathrm{H}_2 & \rightarrow \mathrm{H}_2^+ + \mathrm{e}^-\end{split}\]

in a network, with the former occurring at a rate per particle equal to the rate of primary cosmic ray ionizations, and the other at a rate per particle that is twice the rate of primary ionizations, we would define:

from despotic.chemistry.reactions import cr_reactions
specList = ["H", "H2", "H+", "H2+", "e-"]
reactions \
   = [ { "spec" : ["H", "H+", "e-"], "stoich" : [-1, 1, 1],
         "rate" : 1.0 },
       { "spec" : ["H2", "H2+", "e-"], "stoich" : [-1, 1, 1],
         "rate" : 2.0 } ]
cr = cr_reactions(specList, reactions)

Once a cr_reactions object is instantiated, it can be used to compute the rates of change all species using the cr_reactions.dxdt routine:

dxdt = cr.dxdt(x, n, ionrate)

Here x is a numpy array giving the current abundances, n is the number density of H nuclei, and ionrate is the cosmic ray primary ionization rate.

Note that, in most cases, the variable n will not be used, because it is needed only if there is more than one reactant on the left hand side of a reaction. It is provided to enable the case where a cosmic ray ionization is followed immediately by another reaction, and the network combines the two steps for speed. For example, the NL99 network combines the two reactions

\[\begin{split}\mathrm{CR} + \mathrm{H}_2 & \rightarrow \mathrm{H}_2^+ + \mathrm{e}^- \\ \mathrm{H}_2^+ + \mathrm{H}_2 & \rightarrow \mathrm{H}_3^+ + \mathrm{H} + \mathrm{e}^-\end{split}\]

into the single super-reaction

\[\mathrm{CR} + 2\mathrm{H}_2 \rightarrow \mathrm{H}_3^+ + \mathrm{H} + \mathrm{e}^-\]

and the rate of this super-reaction does depend on density.


The photoreactions class is a specialized class derived from reaction_matrix that is intended to handle reactions that are driven by FUV photons, and thus have rates proportional to the interstellar radiation field (ISRF) strength, modified by dust and gas shielding, and possibly also by line shielding.

Instantiation of photoreactions takes the same two arguments as reaction_matrix: a list of species, and a list of reactions, each element of which is a dict giving the reactants and stoichiometric factors. The dict also contains three additional keys that are specific to photoreactions: rate gives the reaction rate per reactant per second in an ISRF equal to the unshielded Solar neighborhood value (formally, with strength characterized by \(\chi = 1\) – see Cloud file keys and their meanings). The key av_fac describes the optical depth per unit visual extinction provided by dust for the photons driving the reaction. That is, reaction rates will be reduced by a factor \(\exp(-\mathrm{av\_fac}\times A_V)\). Finally, the key shield_fac, which is optional, can be set equal to a callable that describes the rate by which the reaction rate is reduced due to line shielding. This function can take any number of argument – see below. Thus the final reaction rate per reactant will be equal to

\[\mathrm{reaction\, rate} = \chi \times \mathrm{rate} \times \mathrm{shield\_fac} \times \exp(-\mathrm{av\_fac}\times A_V)\]

As an example, suppose we wished to include the reactions

\[\begin{split}\gamma + \mathrm{C} & \rightarrow \mathrm{C}^+ + \mathrm{e}^- \\ \gamma + \mathrm{CO} & \rightarrow \mathrm{C} + \mathrm{O} \\\end{split}\]

The first reaction occurs at a rate per C atom \(5.1\times 10^{-10}\,\mathrm{s}^{-1}\) in unshielded space, and the optical depth to the photons producing the reaction is \(3.0\times A_V\). There is no significant self-shielding. The second reaction occurs at a rate per CO molecule \(1.7\times 10^{-10}\,\mathrm{s}^{-1}\) in unshielded space, with a dust optical depth equal to \(1.7\times A_V\), and with an additional function describing line shielding called fShield_CO. We could create a photoeactions object containing this reaction by doing:

from despotic.chemistry.reactions import photoreactions
specList = ["C", "C+", "e-", "O", "CO"]
reactions \
   = [ { "spec" : ["C", "C+", "e-"], "stoich" : [-1, 1, 1],
         "rate" : 5.1e-10, "av_fac" : 3.0 },
       { "spec" : ["CO", "C", "O"], "stoich" : [-1, 1, 1],
         "rate" : 1.7e-10, "av_fac" : 1.7,
         "shield_fac" : fShield_CO } ]
phr = photoreactions(specList, reactions)

Once a photoreactions object exists, the rates of change of all species due to the included photoreactions can be computed using the photoreactions.dxdt routine. Usage is as follows:

dxdt = phr.dxdt(x, n, chi, AV,
                shield_args=[[f1_arg1, f1_arg2, ...],
                             [f2_arg1, f2_arg2, ...],
                             ... ],
                shield_kw_args = [ { "f1_kw1" : val1,
                                     "f1_kw2" : val2, ... },
                                   { "f2_kw1" : val1,
                                     "f2_kw2" : val2, ... },
                                   ... ])

Here x is a numpy array giving the current abundances, n is the number density of H nuclei, chi is the unshielded ISRF strength, and AV is the dust visual extinction. The optional arguments shield_args and shield_kw_args are used to pass positional arguments and keyword arguments, respectively, to the shielding functions. Each one is a list. The first entry in the list for shield_args is a list containing the positional arguments to be passed to the first shielding function provided in the reactions dict passed to the constructor. The second entry in shield_args is a list containing positional arguments to be passed to the second shielding function, and so forth. Similarly, the first entry in the shield_kw_args list is a dict containing the keywords and their values to be passed to the first shielding function, etc. The length of the shield_args and shield_kw_args must be equal to the number of shielding functions provided in reactions, but elements of the lists can be set to None if a particular shielding function does not take positional or keyword arguments. Moreover, both shield_args and shield_kw_args are optional. If they are omitted, then all shielding functions are invoked with no positional or keyword arguments, respectively.

Thus in the above example, suppose that the function fShield_CO returns the factor by which the reaction rate is reduced by CO line shielding. It takes two arguments: NH2 and NCO, giving the column densities of \(\mathrm{H}_2\) and \(\mathrm{CO}\), respectively. It also accepts a keyword argument order that specifies the order of interpolation to be used in computing the shielding function from a table. In this case one could compute the rates of change of the abundances via:

dxdt = phr.dxdt(x, n, chi, AV,
                shield_args = [[NH2, NCO]],
                shield_kw_args = [ {"order" : 2} ])

This would call fShield_CO as:

fShield_CO(NH2, NCO, order=2)