MIST is a computational algorithm designed to filter geochemical analyses and identify observations with stoichiometric elemental ratios that match real mineral compositions. The algorithm uses normalized oxide percentages and stoichiometric ratios between elements in a detailed decision-tree approach to identify mineral phases.

Mineral species are recognized in five steps, first as observations that fit in mineral classes, then mineral groups, sub-groups, sub-sub-groups and species. Mixtures of phases or chemical ratios that would not fit a real mineral structure are identified as mixtures. When an observation matches a single mineral we output the name of mineral according to IMA rules and a stoichiometric mineral formula.

The algorithm has been tested on well over 2000 EPMA mineral compositions and currently identifies 150 mineral species with >95% accuracy.

Geochemical data is often more straightforward to collect on geological surfaces on Earth and other planets than mineral data, but it is important to know the mineral phases present for geological interpretation. Geochemical data can be collected at high resolutions using techniques including Electron Micro-Probe Analysis (EPMA), micro-X-Ray Fluorescence (XRF; e.g., PIXL instrument on the Mars 2020 rover Perseverance), Electron Dispersive Spectroscopy (EDS), Inductively coupled plasma mass spectrometry (ICP-MS), and Laser-Induced Breakdown Spectroscopy (LIBS; e.g., ChemCam and SuperCam instruments), among others.

The MIST model is agnostic to the source of the geochemical data as long as it is fairly quantitative (we have a ~10% tolerance value on most element concentrations) and high enough resolution that some minerals may be resolved. The MIST model, and stoichiometric approaches to estimating mineral phases in general, work only when the spot size of the geochemical measurement is smaller than the mineral crystal or grain size.

Currently, the MIST model can use any of these oxides as input: SiO2, TiO2, Al2O3, Cr2O3, FeO, Fe2O3, NiO, MnO, MgO, CaO, Na2O, K2O, P2O5, SO3, F, Cl, V2O3, ZnO, CoO, BaO, SrO, B2O3, PbO, CuO, Sb2O3, As2O5, ThO2, ZrO2, HfO2, Ag2O, Y2O3, La2O3, Ce2O3, or Nd2O3.

The model normalizes the geochemical data to 100%. See “About” page and the data input template (download from “Model” page) for more details.

Option 1: Online

Go to the “Model” page.

Download the excel data input template, and carefully read the instructions on the first sheet.

Register your name, email, and affiliation. You will need to complete these fields each time you come back to the site (this is a work in progress). We will not share your data with anyone else.

Upload your input file, per instructions on the input template.

Run the model!

If the run completes successfully, clicking on the "Download Output" button will begin the download of your results. Information about the columns of the results file can also be found on the instructions page of the input template.

If no file downloads, please use the button to download the Error Log File. Email us at mist@mailman.rice.edu with the Error Log File and your input file for help.

Please contact us if you have any further problems or questions!

Option 2: MATLAB

Navigate to the GitHub repository to download the necessary files and run the code on your personal MATLAB.

MIST can only identify minerals that can be segregated using stoichiometry and that we have encoded into the algorithm*. When you run MIST, the output document will include the version number of MIST that was used in the calculation. This file is a list of minerals encoded into the MIST algorithm and will be updated for new version numbers.

*Please note that MIST will output the IMA mineral name and formula (some formulas are still a work in progress). If multiple mineral names share the same formula and have different structures or different hydration states, MIST may not list all possible mineral name options.