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MOMICE: MOdel for Multiphase Ice ChEmistry

Public version of the MOMICE gas-grain astrochemical code.

MOMICE is a chemical model that computes the temporal evolution of the abundances of chemical species in the gas phase and in interstellar ices. The approach is based on the rate equations introduced by Hasegawa et al. (1992) for a gas-grain treatment and allows you to follow the chemistry with fixed or evolving physical conditions. Among others, two of the main specifications of MOMICE is the multiphase treatment for the formation and desorption of ices and the computation of the reaction probabilities of grain surface reactions with the Eckart model from quantum chemical calculations. MOMICE is based on the GRAINOBLE code developed at IPAG between 2009 and 2012, and further modified and updated between 2013 and 2020.

The MoMICE code, written in Fortran90, has two versions, MOMICE_ASTRO and MOMICE_EXP, designed for "interstellar" and "laboratory" applications, respectively. For each version, the code is accompanied by two Python routines that run and analyse the simulations.

In the "interstellar" context, MOMAPP (MOMice APPlier) allows you to execute MOMICE for 1) individual simulations with constant physical conditions, 2) simulations for evolving physical conditions as function of distance of a central source, 3) model grids in which (constant) physical conditions are explored, 4) sensitivity analyses in which the distributions of key input parameters impacts the uncertainties of the model predictions.

In the "laboratory" context, MOMAPP allows you to compare the theoretical predictions with the results of laboratory experiments in order to find the surface and chemical parameters that give the best fit to the experimental data. Three options are possible: 1) a standard model grid in which a chi-square procedure is performed, 2) using an optimisation algorithm that finds local minima of the chi-square function (best suited for a high number of free parameters), 3) Bayesian inference using a MCMC procedure.

For both applications, MOMAN (MOMice ANalyser) reads the binary files generated by MOMICE and visualises the results via different kinds of figures.

For a full description of the physical and chemical processes included in the model and for some results, please take a look at my thesis manuscript, available here, and the publications listed below.

The instructions files within the momice_astro and momice_exp folders describe the input files needed by MOMICE, how to compile and execute the code, and the routines needed to analyze and visualise the results. The log_history file gives an overview of all previous versions of the code between 2012 and 2018.

Publications:

F. Dulieu, T. Nguyen, E. Congiu, S. Baouche, V. Taquet 2019, Efficient formation route of the pre-biotic molecule formamide on interstellar dust grains, MNRAS, L119 (URL)

V. Taquet, K. Furuya, C. Walsh, E. F. van Dishoeck 2016, A primordial origin for molecular oxygen in comets: A chemical kinetics study of the formation and survival of O2 ice from clouds to disks, MNRAS, 462, 99 (URL)

V. Taquet, E. Wirstrom, S. B. Charnley 2016, Formation and recondensation of complex organic molecules during protostellar luminosity outbursts, ApJ, 821, 46 (URL)

V. Taquet, S. B. Charnley, O. Sipilä 2014, Multilayer Formation and Evaporation of Deuterated Ices in Prestellar and Protostellar Cores, ApJ, 791, 1 (URL)

V. Taquet, P. Peters, C. Kahane, C. Ceccarelli, D. Duflot, C. Toubin, A. Faure, L. Wiesenfeld 2013, Modelling of deuterated water ice formation, A&A, 550, A127 (URL)

V. Taquet, C. Ceccarelli, C. Kahane 2012, Formaldehyde and methanol deuteration in protostars: fossiles from a past fast high density pre-collapse phase, ApJL, 748, L3 (URL)

V. Taquet, C. Ceccarelli, C. Kahane 2012, Multilayer modeling of grain porous surface chemistry I. The GRAINOBLE code, AA, 538, 42 (URL)

Acknowledgments

I would like to thank the following people who helped me developing the model over the years: Cecilia Ceccarelli, Claudine Kahane, Philip Peters, Steve Charnley, Kenji Furuya, Franck Hersant, and Mathieu Van der Swaelmen.

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