From e0538a35107a3c9707e2f49a9e0d467dbb4c4f41 Mon Sep 17 00:00:00 2001 From: gdmiron Date: Thu, 2 Jun 2022 13:44:41 +0200 Subject: [PATCH 01/11] JOSS manuscript --- .github/workflows/draft-pdf.yml | 23 + paper/aq17-thermofun.json | 21638 ++++++++++++++++++++++++++++++ paper/examples.ipynb | 127 + paper/paper.bib | 101 + paper/paper.jats | 466 + paper/paper.md | 104 + paper/paper.pdf | Bin 0 -> 258706 bytes 7 files changed, 22459 insertions(+) create mode 100644 .github/workflows/draft-pdf.yml create mode 100644 paper/aq17-thermofun.json create mode 100644 paper/examples.ipynb create mode 100644 paper/paper.bib create mode 100644 paper/paper.jats create mode 100644 paper/paper.md create mode 100644 paper/paper.pdf diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 0000000..db558c2 --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v2 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. 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"J/(mol*K)" + ], + "values": [ + 29.345325469971 + ] + }, + "sm_volume": { + "errors": [ + 0 + ], + "units": [ + "J/bar" + ], + "values": [ + 2478.9699707031 + ] + }, + "symbol": "O2" + } + ], + "thermodataset": [ + "aq17" + ] +} diff --git a/paper/examples.ipynb b/paper/examples.ipynb new file mode 100644 index 0000000..1fa019d --- /dev/null +++ b/paper/examples.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e3da8efc", + "metadata": {}, + "outputs": [], + "source": [ + "import thermofun as fun" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "13b94f94", + "metadata": {}, + "outputs": [], + "source": [ + "database = fun.Database(\"aq17-thermofun.json\")\n", + "engine = fun.ThermoEngine(database)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f0c85f6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G0 -545301.2878298083 J/mol\n" + ] + } + ], + "source": [ + "# T(K) P(Pa) symbol\n", + "Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, \"Ca+2\")\n", + "print(f'G0 {Ca_ion.gibbs_energy.val} J/mol')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a36c08e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drS of (Cal = Ca+2 + CO3-2) is -259.12288450418015\n", + "drG of (Cal = Ca+2 + CO3-2) is 59914.092580924975\n", + "logK of (Cal = Ca+2 + CO3-2) is -8.988976334909019\n" + ] + } + ], + "source": [ + "# T(K) P(Pa) reaction equation\n", + "R = engine.thermoPropertiesReaction(348.15, 1e5, \"Calcite = Ca+2 + CO3-2\")\n", + "print(f'drS of (Cal = Ca+2 + CO3-2) is {R.reaction_entropy.val}')\n", + "print(f'drG of (Cal = Ca+2 + CO3-2) is {R.reaction_gibbs_energy.val}')\n", + "print(f'logK of (Cal = Ca+2 + CO3-2) is {R.log_equilibrium_constant.val}')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "717d85a2", + "metadata": {}, + "outputs": [], + "source": [ + "batch = fun.ThermoBatch(database)\n", + "batch.setPropertiesUnits([\"temperature\", \"pressure\"],[\"degC\",\"bar\"])\n", + "batch.setPressureIncrement(0,0,0)\n", + "batch.setTemperatureIncrement(0,300, 5)\n", + "substances = [\"Na+\", \"Mg+2\", \"Ca+2\", \"SiO2@\"]\n", + "properties = [\"heat_capacity_cp\",\"entropy\", \"volume\"]\n", + "batch.thermoPropertiesSubstance(substances, properties).toCSV('results.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7721f824", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": false, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..35f3727 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,101 @@ +@article{Kulik2013, + abstract = {Reactive mass transport (RMT) simulation is a powerful numerical tool to advance our understanding of complex geochemical processes and their feedbacks in relevant subsurface systems. Thermodynamic equilibrium defines the baseline for solubility, chemical kinetics, and RMT in general. Efficient RMT simulations can be based on the operator-splitting approach, where the solver of chemical equilibria is called by the mass transport part for each control volume whose composition, temperature, or pressure has changed. Modeling of complex natural systems requires consideration of multiphase-multicomponent geochemical models that include nonideal solutions (aqueous electrolytes, fluids, gases, solid solutions, and melts). Direct Gibbs energy minimization (GEM) methods have numerous advantages for the realistic geochemical modeling of such fluid-rock systems. Substantial improvements and extensions to the revised GEM interior point method algorithm based on Karpov's convex programming approach are described, as implemented in the GEMS3K C/C++ code, which is also the numerical kernel of GEM-Selektor v.3 package (http://gems.web.psi.ch). GEMS3K is presented in the context of the essential criteria of chemical plausibility, robustness of results, mass balance accuracy, numerical stability, speed, and portability to high-performance computing systems. The stand-alone GEMS3K code can treat very complex chemical systems with many nonideal solution phases accurately. It is fast, delivering chemically plausible and accurate results with the same or better mass balance precision as that of conventional speciation codes. GEMS3K is already used in several coupled RMT codes (e.g., OpenGeoSys-GEMS) capable of high-performance computing. Β© 2012 Springer Science+Business Media B.V.}, + author = {Dmitrii A. Kulik and Thomas Wagner and Svitlana V. Dmytrieva and Georg Kosakowski and Ferdinand F. Hingerl and Konstantin V. Chudnenko and Urs R. Berner}, + doi = {10.1007/s10596-012-9310-6}, + issn = {14200597}, + issue = {1}, + journal = {Computational Geosciences}, + keywords = {Fluid-rock interaction,Geochemical modeling,Gibbs energy minimization,Nonideal systems,Reactive mass transport}, + month = {8}, + pages = {1-24}, + publisher = {Springer Netherlands}, + title = {GEM-Selektor geochemical modeling package: Revised algorithm and GEMS3K numerical kernel for coupled simulation codes}, + volume = {17}, + url = {http://link.springer.com/10.1007/s10596-012-9310-6}, + year = {2013}, +} +@article{Miron2019, + abstract = {

Thermodynamic properties of aqueous species are essential for modeling of fluid-rock interaction processes. The Helgeson-Kirkham-Flowers (HKF) model is widely used for calculating standard state thermodynamic properties of ions and complexes over a wide range of temperatures and pressures. To do this, the HKF model requires thermodynamic and electrostatic models of water solvent. In this study, we investigate and quantify the impact of choosing different models for calculating water solvent volumetric and dielectric properties, on the properties of aqueous species calculated using the HKF model. We identify temperature and pressure conditions at which the choice of different models can have a considerable effect on the properties of aqueous species and on fluid mineral equilibrium calculations. The investigated temperature and pressure intervals are 25–1000Β°C and 1–5 kbar, representative of upper to middle crustal levels, and of interest for modeling ore-forming processes. The thermodynamic and electrostatic models for water solvent considered are: Haar, Gallagher and Kell (1984), Wagner and Pruß (2002), and Zhang and Duan (2005), to calculate water volumetric properties, and Johnson and Norton (1991), Fernandez and others (1997), and Sverjensky and others (2014), to calculate water dielectric properties. We observe only small discrepancies in the calculated standard partial molal properties of aqueous species resulting from using different water thermodynamic models. However, large differences in the properties of charged species can be observed at higher temperatures (above 500Β°C) as a result of using different electrostatic models. Depending on the aqueous speciation and the reactions that control the chemical composition, the observed differences can vary. The discrepancy between various electrostatic models is attributed to the scarcity of experimental data at high temperatures. These discrepancies restrict the reliability of the geochemical modeling of hydrothermal and ore formation processes, and the retrieval of thermodynamic parameters from experimental data at elevated temperatures and pressures.

}, + author = {G. D. Miron and Allan M. M. Leal and Alina Yapparova}, + doi = {10.1155/2019/5750390}, + issn = {1468-8115}, + journal = {Geofluids}, + month = {2}, + pages = {1-24}, + publisher = {Hindawi}, + title = {Thermodynamic Properties of Aqueous Species Calculated Using the HKF Model: How Do Different Thermodynamic and Electrostatic Models for Solvent Water Affect Calculated Aqueous Properties?}, + volume = {2019}, + url = {https://www.hindawi.com/journals/geofluids/2019/5750390/}, + year = {2019}, +} +@article{Miron2020, + abstract = {Most of the available thermodynamic data concerning radioactive waste disposal are restricted to values of reaction equilibrium constants (logK298Β°) at 25 Β°C and 1 bar. Simple estimation methods such as isocoulombic reactions can be used for extrapolating the properties of reactions involving aqueous species and minerals to elevated temperatures. The aim of this study was to validate the applicability of various alternative isocoulombic reactions to estimate logKTΒ° values of aqueous complexation reactions for lanthanides and actinides to elevated temperatures while taking advantage of new additional literature data, and to identify criteria for choosing the β€œbest” reactions. For each chemical species of interest, a systematic approach using dedicated software and database allowed us to identify the isocoulombic reactions and types of extrapolation that yield the best estimates of standard thermodynamic properties at elevated temperatures, when very limited or no experimental data are available. We have tested aqueous complexation reactions for selected lanthanides and actinides of different valences with chloride, fluoride, sulfate, carbonate, nitrate, phosphate and silicate ligands. β€œModel” complexation reactions, having known temperature trends, were systematically combined with complex formation reactions of interest whose temperature trends are unknown, into many alternative isocoulombic reactions. For each ion, we investigated which of the generated isocoulombic reactions provide the best estimates for logeKTΒ° of the reaction of interest at elevated temperatures in order to compile the guidelines for choosing the optimal ones, then applying these guidelines to β€œprediction” subsets. In most cases, knowing only logeKTΒ° at 25 Β°C (for the reaction of interest), it was possible to obtain rather accurate estimates of logeKTΒ° values at elevated temperatures using isocoulombic reactions that exchange ions with similar charge and hydration properties (hydrated ionic radius and structure of the hydration shell) and known logmKTΒ° of model reactions. These ions and their complexes interact with the solvent in comparable ways, so that their similar heat capacity and entropy effects largely cancel out on both sides of an β€œoptimal” isocoulombic reaction.}, + author = {G. D. Miron and Dmitrii A. Kulik and Tres Thoenen}, + doi = {10.1016/j.gca.2020.07.020}, + issn = {00167037}, + journal = {Geochimica et Cosmochimica Acta}, + keywords = {Actinides,Aqueous complexation,Isocoulombic reactions,Lanthanides,Temperature extrapolations}, + month = {10}, + pages = {119-142}, + publisher = {Elsevier Ltd}, + title = {Generating isocoulombic reactions as a tool for systematic evaluation of temperature trends of thermodynamic properties: Application to aquocomplexes of lanthanides and actinides}, + volume = {286}, + year = {2020}, +} +@article{Leal2017, + abstract = {

We present an overview of novel numerical methods for chemical equilibrium and kinetic calculations for complex non-ideal multiphase systems. The methods we present for equilibrium calculations are based either on Gibbs energy minimization (GEM) calculations or on solving the system of extended law of mass-action (xLMA) equations. In both methods, no

}, + author = {Allan M. M. Leal and Dmitrii A. Kulik and William R. Smith and Martin O. Saar}, + doi = {10.1515/pac-2016-1107}, + issn = {1365-3075}, + issue = {5}, + journal = {Pure and Applied Chemistry}, + keywords = {17,ISSP,chemical equilibrium,chemical kinetics,geochemical modeling,numerical methods,reactive transport}, + month = {1}, + pages = {597-643}, + publisher = {De Gruyter}, + title = {An overview of computational methods for chemical equilibrium and kinetic calculations for geochemical and reactive transport modeling}, + volume = {89}, + url = {http://www.degruyter.com/view/j/pac.2017.89.issue-5/pac-2016-1107/pac-2016-1107.xml}, + year = {2017}, +} +@article{Leal2016, + abstract = {We present an extended law of mass-action (xLMA) method for multiphase equilibrium calculations and apply it in the context of reactive transport modeling. This extended LMA formulation differs from its conventional counterpart in that (i) it is directly derived from the Gibbs energy minimization (GEM) problem (i.e., the fundamental problem that describes the state of equilibrium of a chemical system under constant temperature and pressure); and (ii) it extends the conventional mass-action equations with Lagrange multipliers from the Gibbs energy minimization problem, which can be interpreted as stability indices of the chemical species. Accounting for these multipliers enables the method to determine all stable phases without presuming their types (e.g., aqueous, gaseous) or their presence in the equilibrium state. Therefore, the here proposed xLMA method inherits traits of Gibbs energy minimization algorithms that allow it to naturally detect the phases present in equilibrium, which can be single-component phases (e.g., pure solids or liquids) or non-ideal multi-component phases (e.g., aqueous, melts, gaseous, solid solutions, adsorption, or ion exchange). Moreover, our xLMA method requires no technique that tentatively adds or removes reactions based on phase stability indices (e.g., saturation indices for minerals), since the extended mass-action equations are valid even when their corresponding reactions involve unstable species. We successfully apply the proposed method to a reactive transport modeling problem in which we use PHREEQC and GEMS as alternative backends for the calculation of thermodynamic properties such as equilibrium constants of reactions, standard chemical potentials of species, and activity coefficients. Our tests show that our algorithm is efficient and robust for demanding applications, such as reactive transport modeling, where it converges within 1–3 iterations in most cases. The proposed xLMA method is implemented in Reaktoro, a unified open-source framework for modeling chemically reactive systems.}, + author = {Allan M.M. Leal and Dmitrii A. Kulik and Georg Kosakowski and Martin O. Saar}, + doi = {10.1016/j.advwatres.2016.08.008}, + issn = {03091708}, + journal = {Advances in Water Resources}, + keywords = {Equilibrium,Gibbs energy minimization,LMA,Multiphase,Reactive transport,Speciation}, + month = {10}, + pages = {405-422}, + publisher = {Elsevier Ltd}, + title = {Computational methods for reactive transport modeling: An extended law of mass-action, xLMA, method for multiphase equilibrium calculations}, + volume = {96}, + year = {2016}, +} +@article{Miron2016, + abstract = {? 2016 Elsevier Ltd.A large amount of critically evaluated experimental data on mineral solubility, covering the entire Na-K-Al-Si-O-H-Cl system over wide ranges in temperature and pressure, was used to simultaneously refine the standard state Gibbs energies of aqueous ions and complexes in the framework of the revised Helgeson-Kirkham-Flowers equation of state. The thermodynamic properties of the solubility-controlling minerals were adopted from the internally consistent dataset of Holland and Powell (2002; Thermocalc dataset ds55). The global optimization of Gibbs energies of aqueous species, performed with the GEMSFITS code (Miron et al., 2015), was set up in such a way that the association equilibria for ion pairs and complexes, independently derived from conductance and potentiometric data, are always maintained. This was achieved by introducing reaction constraints into the parameter optimization that adjust Gibbs energies of complexes by their respective Gibbs energy effects of reaction, whenever the Gibbs energies of reactant species (ions) are changed. The optimized thermodynamic dataset is reported with confidence intervals for all parameters evaluated by Monte Carlo trial calculations. The new thermodynamic dataset is shown to reproduce all available fluid-mineral phase equilibria and mineral solubility data with good accuracy and precision over wide ranges in temperature (25-800 ?C), pressure (1 bar to 5 kbar) and composition (salt concentrations up to 5 molal). The global data optimization process adopted in this study can be readily repeated any time when extensions to new chemical elements and species are needed, when new experimental data become available, or when a different aqueous activity model or equation of state should be used. This work serves as a proof of concept that our optimization strategy is feasible and successful in generating a thermodynamic dataset reproducing all fluid-mineral and aqueous speciation equilibria in the Na-K-Al-Si-O-H-Cl system within their experimental uncertainties. The new dataset resolves the long-standing discrepancies between thermodynamic data of minerals and those of aqueous ions and complexes, by achieving an astonishing degree of consistency between a large number of fluid-mineral equilibrium data. All of this at the expense of changing the standard state properties of aqueous species, mainly the Gibbs energy of formation. Using the same strategy, the core dataset for the system Na-K-Al-Si-O-H-Cl can be extended with additional rock-forming elements such as Ca, Mg, Fe, Mn, Ti, S, C, B. In future, the standard-state properties of minerals and aqueous species should be simultaneously optimized, to create the next-generation of fully internally consistent data for fluid-mineral equilibria. Although we employ the widely used HKF equations for this study, the same computational approach can be readily applied to any other speciation-based equation of state for multicomponent aqueous solutions.}, + author = {G.D. Miron and T. Wagner and D.A. Kulik and C.A. Heinrich}, + doi = {10.1016/j.gca.2016.04.026}, + issn = {00167037}, + journal = {Geochimica et Cosmochimica Acta}, + keywords = {[Aqueous species, Fluid-rock interaction, Hydrothe}, + title = {Internally consistent thermodynamic data for aqueous species in the system Na-K-Al-Si-O-H-Cl}, + volume = {187}, + year = {2016}, +} +@article{Miron2017, + abstract = {This study presents an internally consistent thermodynamic dataset for aqueous species in the system Ca-Mg-Na-K-Al-Si-O-H-C-Cl, obtained by adding species of calcium, magnesium and carbon to the core system Na-K-Al-Si-O-H-Cl (Miron and others, 2016). Critically evaluated experimental data on mineral solubility (Ca and Mg hydroxides, Ca and Mg silicates, anorthite, Ca and Mg carbonates) in water and aqueous electrolyte solutions over wide ranges in temperature and pressure were added to the database of experimental data. The complete experimental dataset was then used to simultaneously refine the standard state Gibbs energies of all aqueous ions and complexes in the framework of the revised Helgeson-Kirkham-Flowers (HKF) equation of state. The thermodynamic properties of the solubility-controlling minerals were accepted from the internally consistent dataset of Holland and Powell (1998; updated Thermocalc dataset ds55). The association equilibria of important hydroxide, chloride, carbonate and silicate complexes were critically reviewed, and their standard state properties and HKF parameters were independently derived from conductance, potentiometric and, in a few cases, solubility measurements. In a global optimization of standard Gibbs energies of aqueous species, performed with the GEMSFITS code (Miron and others, 2015), the association equilibria for aqueous complexes were always maintained. The new thermodynamic dataset reproduces all available fluidmineral phase equilibria and mineral solubility data in the system Ca-Mg-Na-K-Al-Si-OH-C-Cl with good accuracy over wide ranges in temperature (25-800 Β°C), pressure (1 bar - 5 kbar) and composition (salt concentrations up to 5 molal). This makes it possible to perform geochemical and reactive transport modeling of processes in natural and engineered georeservoirs over wide ranges of conditions with an unprecedented level of accuracy and reliability and to address processes of fluid flow and fluid-rock interaction in the Earth's crust from a new perspective. Using the same strategy as applied in the present study, the internally consistent thermodynamic dataset can be further extended with additional major and trace elements, and the data refinement process can be repeated when new experimental data or next-generation equation of state or activity models for aqueous solutions become available.}, + author = {G.D. Miron and T. Wagner and D.A. Kulik and B. Lothenbach}, + doi = {10.2475/07.2017.01}, + issn = {00029599}, + issue = {7}, + journal = {American Journal of Science}, + keywords = {Aqueous species,Experimental data evaluation,Fluid-rock interaction,Hydrothermal geochemical modeling,Optimization,Thermodynamic database}, + pages = {755-806}, + title = {An internally consistent thermodynamic dataset for aqueous species in the system Ca-Mg-Na-K-Al-Si-O-H-C-Cl to 800 Β°c and 5 kbar}, + volume = {317}, + year = {2017}, +} + + + diff --git a/paper/paper.jats b/paper/paper.jats new file mode 100644 index 0000000..156126a --- /dev/null +++ b/paper/paper.jats @@ -0,0 +1,466 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +0 +N/A + +ThermoFun: A library that delivers standard thermodynamic +properties of substances and reactions at temperature and pressure of +interest + + + +0000-0002-6923-7300 + +Miron +George Dan + + +* + + + +Leal +Allan M. M. + + + + + +Dmytrieva +S. V. + + + + + +Kulik +Dmitrii A. + + + + + +Laboratory for Waste Management LES, Paul Scherrer +Institut, 5232 Villigen, Switzerland + + + + +Geothermal Energy and Geofluids Group, Institute of +Geophysics, ETH Zurich, Switzerland + + + + +Cosylab + + + + +* E-mail: + + +10 +5 +2022 + +ΒΏVOL? +ΒΏISSUE? +ΒΏPAGE? + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +C++ +Python +thermodynamics +equations of state +geochemistry +materials + + + + + + Summary +

ThermoFun + thermohub.org/thermofun + open source library provides standard thermodynamic properties of + substances and reactions calculated at given temperature and pressure. + The library can be used from C++ and Python to do single or bach + calculations and can be coupled to to any modeling code which requires + thermodynamic data as input or to codes for model parameter fitting. + Results can be written to file in a table or other formats or passed + in memory to coupled codes that call the library. For its + calculations, ThermoFun can use input properties and model parameters + of different thermodynamic datasets that are read from local files or + are retrieved via remote access to ThermoHub + (thermohub.org/thermohub). + The library contains a comprehensive collection of models and + equations of state for: solid, aqueous, gaseous, and melt substances, + and their reactions. A certain model or equation of state is not + present? ThermoFun library is extendable with new methods and + equations of state, taking advantage of the modular structure. The + open source code is available at: + https://github.com/thermohub/thermofun

+ + + Statement of need +

Thermodynamic modelling provides valuable insight into chemical + systems and allows us to investigate processes that occur under + conditions and time scales that cannot be reached in laboratory + settings. An unavoidable step in any realistic application of + geochemical thermodynamic modelling is the calculation of standard + thermodynamic properties of all involved substances and reactions at + temperature and pressure of interest + (Leal + et al., 2017). This is done using equations of state that + relate thermodynamic properties to temperature and pressure. The + quality of thermodynamic modeling results are strongly influenced by + the predictive capability of the method and the availability, + accuracy, precision and consistency of input parameters in + thermodynamic datasets.

+

Many different methods have been developed that allow extrapolation + of standard thermodynamic properties over a wide range of conditions. + These methods can have complex mathematical formulations, a large + number of input parameters, can require iterative numerical procedures + or a combination of several methods is be necessary to calculate + properties of a certain type of substance. For example, the properties + of the solvent are used to calculate the properties of the solutes. + Selective implementations of extrapolation methods and equations of + state can be found in existing thermodynamic modeling codes or as + standalone codes that work with specific thermodynamic datasets and + file formats. This makes it difficult to retrieve properties + calculated with different methods and use them in different codes, or + to investigate, compare and tabulate thermodynamic data coming from + different sources. With many existing thermodynamic datasets, + equations of state, modeling codes and file formats, it can be + difficult and time consuming to collect such data and to apply + suitable methods in modeling calculations.

+ +

Example of properties of substances and reactions + calculated using ThermoFun as function of temperature at saturated + water vapor + pressure.

+ + +

The open-source ThermoFun library, provides standard thermodynamic + properties of substances and reactions + FigureΒ 1. These + are calculated using the extensive collection of methods available in + ThermoFun using input properties and parameters from different + thermodynamic datasets retrievable from the ThermoHub online database + or provided as local files. The library is extendable with new methods + and has access to the evolving collection of thermodynamic datasets + from ThermoHub. These datasets contain reference thermodynamic data + applicable in various studies on hydrothermal processes, cementitious + materials, nuclear engineering, waste incineration management and + disposal, and other (geo)chemical environments. Access to many + thermodynamic datasets, methods and equations of state, allows for + more efficient use of these in modeling codes, or to optimize/fit the + methods’s parameters, evaluate its performance, range of applicability + and accuracy. ThermoFun provides the flexibility of using + thermodynamic datasets and appropriate methods, or a combination of + methods that are best suited for different classes of substances + necessary in various modeling applications.

+

The library can be linked to any C++ or Python geochemical + modelling code and used as source of standard thermodynamic + properties, or can be simply used for tabulating thermodynamic + properties of substances and reactions. ThermoFun was used to + calculate the properties of reactants and reactions at elevated + temperatures when evaluating the use of isocolomibc reactions to + estimate properties of aqueous complexation reactions for lanthanides + and actinides + (Miron + et al., 2020). The library was used to evaluate the effect of + using different equations of state for water solvent on the calculated + properties of dissolved aqueous + species(Miron + et al., 2019). ThermoFun is already used as a source of + thermodynamic data in GEMS codes + gems.web.psi.ch + (Kulik + et al., 2013) and in Reaktoro geochemical modeling framework + reaktoro.org + (Leal + et al., 2016).

+ + + Features +

The main classes of ThermoFun are Database, + ThermoEngine, and + ThermoBatch. + ThermoEngine, and + ThermoBatch are initialized with the + Database. The Database + is initialized with a thermodynamic dataset. A thermodynamic dataset + is also known in the (geo)chemical literature as a thermodynamic + database with records of elements, substances and reactions that + contain various data like symbol, formula, reaction equation their + thermodynamic properties and model parameters that are needed in the + calculations, etc. Theses data are compiled, critically selected from + literature and may be also fitted against experimental data to be used + in modeling codes to calculate chemical equilibrium in different + chemical systems. Thermodynamic datasets used in ThermoFun can be read + locally from files or retrieved via remote access to ThermoHub online + database. ThermoHub contains a collection of thermodynamic datasets + that are widely used in various modeling applications + thermohub.org/thermohub. + The online ThermoHub database is actively extended with existing, and + newly updated and developed datasets prepared by thermodynamic + database experts and thus made available for calculations with + ThermoFun. In general, the calculations are done using one consistent + dataset but there is also the possibility to use custom datasets, + combine them and append additional data from user maintained files. + The data format of ThermoHub is JSON (Java + Script Object Notation) and using + ThermoHubClient thermodynamic datasets are + retrieved into a simplified ThermoFun format + and saved to text files that can edited and extended.

+

ThermoFun engine class ThermoEngine contains + functions that return the standard state thermodynamic properties of a + substance or reaction at the given temperature and pressure. For water + solvent, additional specific properties like water density, dielectric + constant can be retrieved. A low level fine grained access up to + individual methods is also possible. + ThermoBatch class can be used to do batch + calculations, for a list of substances, reactions, and temperature and + pressure intervals. ThermoBatch can be also + used to output the results in tabulated comma separated values (CSV) + or other data formats.

+

Reaction properties can be calculated for reaction records present + in the thermodynamic dataset in this case the extrapolation at + temperature and pressure is done using the method and input parameters + available in the reaction record. Another option is to provide + reaction equations written using symbols of substances available in + the thermodynamic dataset (e.g., Calcite + + + ⇋ + Ca + + 2+ + + CO + + 3βˆ’2). + In this case the properties of the reactions are calculated from the + properties of the reactants extrapolated based on the methods defined + in their records. Substance properties are calculated using the + specified equation of state and the input parameters provided in the + dataset record. Another option is to set a reaction that defines the + substance properties. In the case of a reaction dependent substance, + the properties are calculated from the reaction properties and the + properties of the other reactants that take part in the reaction. One + dataset can contain a combination of substances, reaction dependent + substances and reactions that have their properties calculated + consistently calculated using a recursive method.

+ + + Examples +

Loading the aq17 thermodynamic dataset + (Miron + et al., 2016, + 2017) and + calculating the properties of a substance (calcium aqueous ion + + + Ca2+):

+ import thermofun as fun + database = fun.Database("aq17-thermofun.json") + engine = fun.ThermoEngine(database) + # T(K) P(Pa) symbol + Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, "Ca+2") + print(f'G0 {Ca_ion.gibbs_energy.val} J/mol') +

G0 -545301.2878298083 J/mol

+

Calculating the properties of a reaction given as a reaction + equation (calcite dissolution):

+ # T(K) P(Pa) reaction equation + R = engine.thermoPropertiesReaction(348.15, 1e5, "Calcite = Ca+2 + CO3-2") + print(f'drS of (Cal = Ca+2 + CO3-2) is {R.reaction_entropy.val}') + print(f'drG of (Cal = Ca+2 + CO3-2) is {R.reaction_gibbs_energy.val}') + print(f'logK of (Cal = Ca+2 + CO3-2) is {R.log_equilibrium_constant.val}') + drS of (Cal = Ca+2 + CO3-2) is -259.12288450418015 +drG of (Cal = Ca+2 + CO3-2) is 59914.092580924975 +logK of (Cal = Ca+2 + CO3-2) is -8.988976334909019 +

Using the batch class to do sequential calculations and output the + results to a CSV file:

+ batch = fun.ThermoBatch(database) + batch.setPropertiesUnits(["temperature", "pressure"],["degC","bar"]) + batch.setPressureIncrement(0,0,0) + batch.setTemperatureIncrement(0,300, 5) + substances = ["Na+", "Mg+2", "Ca+2", "SiO2@"] + properties = ["heat_capacity_cp","entropy", "volume"] + batch.thermoPropertiesSubstance(substances, properties).toCSV('results.csv') +

Jupyter notebooks with examples on how to use + ThermoFun can be found here + https://github.com/thermohub/thermofun-jupyter. + More complex scripts and routines can be coded that call + ThermoFun. For example, + ThermoFun can be coupled to a phase diagram + plotter, a chemical equilibrium solver that require properties of all + components in the system, or a parameter optimization routine for + fitting the parameters of the methods available in ThermoFun.

+ + + + + + + + KulikDmitrii A. + WagnerThomas + DmytrievaSvitlana V. + KosakowskiGeorg + HingerlFerdinand F. + ChudnenkoKonstantin V. + BernerUrs R. + + GEM-selektor geochemical modeling package: Revised algorithm and GEMS3K numerical kernel for coupled simulation codes + Computational Geosciences + Springer Netherlands + 201308 + 17 + 14200597 + http://link.springer.com/10.1007/s10596-012-9310-6 + 10.1007/s10596-012-9310-6 + 1 + 24 + + + + + + MironG. D. + LealAllan M. M. + YapparovaAlina + + Thermodynamic properties of aqueous species calculated using the HKF model: How do different thermodynamic and electrostatic models for solvent water affect calculated aqueous properties? + Geofluids + Hindawi + 201902 + 2019 + 1468-8115 + https://www.hindawi.com/journals/geofluids/2019/5750390/ + 10.1155/2019/5750390 + 1 + 24 + + + + + + MironG. D. + KulikDmitrii A. + ThoenenTres + + Generating isocoulombic reactions as a tool for systematic evaluation of temperature trends of thermodynamic properties: Application to aquocomplexes of lanthanides and actinides + Geochimica et Cosmochimica Acta + Elsevier Ltd + 202010 + 286 + 00167037 + 10.1016/j.gca.2020.07.020 + 119 + 142 + + + + + + LealAllan M. M. + KulikDmitrii A. + SmithWilliam R. + SaarMartin O. + + An overview of computational methods for chemical equilibrium and kinetic calculations for geochemical and reactive transport modeling + Pure and Applied Chemistry + De Gruyter + 201701 + 89 + 1365-3075 + http://www.degruyter.com/view/j/pac.2017.89.issue-5/pac-2016-1107/pac-2016-1107.xml + 10.1515/pac-2016-1107 + 597 + 643 + + + + + + LealAllan M. M. + KulikDmitrii A. + KosakowskiGeorg + SaarMartin O. + + Computational methods for reactive transport modeling: An extended law of mass-action, xLMA, method for multiphase equilibrium calculations + Advances in Water Resources + Elsevier Ltd + 201610 + 96 + 03091708 + 10.1016/j.advwatres.2016.08.008 + 405 + 422 + + + + + + MironG. D. + WagnerT. + KulikD. A. + HeinrichC. A. + + Internally consistent thermodynamic data for aqueous species in the system na-k-al-si-o-h-cl + Geochimica et Cosmochimica Acta + 2016 + 187 + 00167037 + 10.1016/j.gca.2016.04.026 + + + + + + MironG. D. + WagnerT. + KulikD. A. + LothenbachB. + + An internally consistent thermodynamic dataset for aqueous species in the system ca-mg-na-k-al-si-o-h-c-cl to 800 Β°c and 5 kbar + American Journal of Science + 2017 + 317 + 00029599 + 10.2475/07.2017.01 + 755 + 806 + + + + +
diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 0000000..9bc6390 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,104 @@ +--- +title: 'ThermoFun: A library that delivers standard thermodynamic properties of substances and reactions at temperature and pressure of interest' +tags: + - C++ + - Python + - thermodynamics + - equations of state + - geochemistry + - materials +authors: + - name: George Dan Miron^[corresponding author] + affiliation: 1 + orcid: 0000-0002-6923-7300 + - name: Allan M. M. Leal + affiliation: 2 + - name: S.V. Dmytrieva + affiliation: 3 + - name: Dmitrii A. Kulik + affiliation: 1 +affiliations: + - index: 1 + name: Laboratory for Waste Management LES, Paul Scherrer Institut, 5232 Villigen, Switzerland + - index: 2 + name: Geothermal Energy and Geofluids Group, Institute of Geophysics, ETH Zurich, Switzerland + - index: 3 + name: Cosylab +date: "10 May 2022" +bibliography: paper.bib +--- + +# Summary + +ThermoFun [thermohub.org/thermofun](https://thermohub.org/thermofun) open source library provides standard thermodynamic properties of substances and reactions calculated at given temperature and pressure. The library can be used from C++ and Python to do single or bach calculations and can be coupled to to any modeling code which requires thermodynamic data as input or to codes for model parameter fitting. Results can be written to file in a table or other formats or passed in memory to coupled codes that call the library. For its calculations, ThermoFun can use input properties and model parameters of different thermodynamic datasets that are read from local files or are retrieved via remote access to ThermoHub ([thermohub.org/thermohub](https://thermohub.org/thermohub)). The library contains a comprehensive collection of models and equations of state for: solid, aqueous, gaseous, and melt substances, and their reactions. A certain model or equation of state is not present? ThermoFun library is extendable with new methods and equations of state, taking advantage of the modular structure. The open source code is available at: [https://github.com/thermohub/thermofun](https://github.com/thermohub/thermofun) + +# Statement of need + +Thermodynamic modelling provides valuable insight into chemical systems and allows us to investigate processes that occur under conditions and time scales that cannot be reached in laboratory settings. An unavoidable step in any realistic application of geochemical thermodynamic modelling is the calculation of standard thermodynamic properties of all involved substances and reactions at temperature and pressure of interest [@Leal2017]. This is done using equations of state that relate thermodynamic properties to temperature and pressure. The quality of thermodynamic modeling results are strongly influenced by the predictive capability of the method and the availability, accuracy, precision and consistency of input parameters in thermodynamic datasets. + +Many different methods have been developed that allow extrapolation of standard thermodynamic properties over a wide range of conditions. These methods can have complex mathematical formulations, a large number of input parameters, can require iterative numerical procedures or a combination of several methods is be necessary to calculate properties of a certain type of substance. For example, the properties of the solvent are used to calculate the properties of the solutes. Selective implementations of extrapolation methods and equations of state can be found in existing thermodynamic modeling codes or as standalone codes that work with specific thermodynamic datasets and file formats. This makes it difficult to retrieve properties calculated with different methods and use them in different codes, or to investigate, compare and tabulate thermodynamic data coming from different sources. With many existing thermodynamic datasets, equations of state, modeling codes and file formats, it can be difficult and time consuming to collect such data and to apply suitable methods in modeling calculations. + +![Example of properties of substances and reactions calculated using ThermoFun as function of temperature at saturated water vapor pressure.\label{fig:properties}](figure1.png) + +The open-source ThermoFun library, provides standard thermodynamic properties of substances and reactions \autoref{fig:properties}. These are calculated using the extensive collection of methods available in ThermoFun using input properties and parameters from different thermodynamic datasets retrievable from the ThermoHub online database or provided as local files. The library is extendable with new methods and has access to the evolving collection of thermodynamic datasets from ThermoHub. These datasets contain reference thermodynamic data applicable in various studies on hydrothermal processes, cementitious materials, nuclear engineering, waste incineration management and disposal, and other (geo)chemical environments. Access to many thermodynamic datasets, methods and equations of state, allows for more efficient use of these in modeling codes, or to optimize/fit the methods’s parameters, evaluate its performance, range of applicability and accuracy. ThermoFun provides the flexibility of using thermodynamic datasets and appropriate methods, or a combination of methods that are best suited for different classes of substances necessary in various modeling applications. + +The library can be linked to any C++ or Python geochemical modelling code and used as source of standard thermodynamic properties, or can be simply used for tabulating thermodynamic properties of substances and reactions. ThermoFun was used to calculate the properties of reactants and reactions at elevated temperatures when evaluating the use of isocolomibc reactions to estimate properties of aqueous complexation reactions for lanthanides and actinides [@Miron2020]. The library was used to evaluate the effect of using different equations of state for water solvent on the calculated properties of dissolved aqueous species[@Miron2019]. +ThermoFun is already used as a source of thermodynamic data in GEMS codes [gems.web.psi.ch](https://gems.web.psi.ch/) [@Kulik2013] and in Reaktoro geochemical modeling framework [reaktoro.org](https://reaktoro.org) [@Leal2016]. + +# Features + +The main classes of ThermoFun are `Database`, `ThermoEngine`, and `ThermoBatch`. `ThermoEngine`, and `ThermoBatch` are initialized with the `Database`. The `Database` is initialized with a thermodynamic dataset. A thermodynamic dataset is also known in the (geo)chemical literature as a thermodynamic database with records of elements, substances and reactions that contain various data like symbol, formula, reaction equation their thermodynamic properties and model parameters that are needed in the calculations, etc. Theses data are compiled, critically selected from literature and may be also fitted against experimental data to be used in modeling codes to calculate chemical equilibrium in different chemical systems. Thermodynamic datasets used in ThermoFun can be read locally from files or retrieved via remote access to ThermoHub online database. ThermoHub contains a collection of thermodynamic datasets that are widely used in various modeling applications [thermohub.org/thermohub](https://thermohub.org/thermohub/thermohub/#thermodatasets). The online ThermoHub database is actively extended with existing, and newly updated and developed datasets prepared by thermodynamic database experts and thus made available for calculations with ThermoFun. In general, the calculations are done using one consistent dataset but there is also the possibility to use custom datasets, combine them and append additional data from user maintained files. The data format of `ThermoHub` is JSON (Java Script Object Notation) and using `ThermoHubClient` thermodynamic datasets are retrieved into a simplified `ThermoFun` format and saved to text files that can edited and extended. + +ThermoFun engine class `ThermoEngine` contains functions that return the standard state thermodynamic properties of a substance or reaction at the given temperature and pressure. For water solvent, additional specific properties like water density, dielectric constant can be retrieved. A low level fine grained access up to individual methods is also possible. +`ThermoBatch` class can be used to do batch calculations, for a list of substances, reactions, and temperature and pressure intervals. `ThermoBatch` can be also used to output the results in tabulated comma separated values (CSV) or other data formats. + +Reaction properties can be calculated for reaction records present in the thermodynamic dataset in this case the extrapolation at temperature and pressure is done using the method and input parameters available in the reaction record. Another option is to provide reaction equations written using symbols of substances available in the thermodynamic dataset (e.g., Calcite $\leftrightharpoons$ Ca$^{2+}$ + CO$_{3}^{-2}$). In this case the properties of the reactions are calculated from the properties of the reactants extrapolated based on the methods defined in their records. Substance properties are calculated using the specified equation of state and the input parameters provided in the dataset record. Another option is to set a reaction that defines the substance properties. In the case of a reaction dependent substance, the properties are calculated from the reaction properties and the properties of the other reactants that take part in the reaction. One dataset can contain a combination of substances, reaction dependent substances and reactions that have their properties calculated consistently calculated using a recursive method. + +# Examples + +Loading the `aq17` thermodynamic dataset [@Miron2016;@Miron2017] and calculating the properties of a substance (calcium aqueous ion $Ca^{2+}$): + +```python + import thermofun as fun + database = fun.Database("aq17-thermofun.json") + engine = fun.ThermoEngine(database) + # T(K) P(Pa) symbol + Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, "Ca+2") + print(f'G0 {Ca_ion.gibbs_energy.val} J/mol') +``` + + `G0 -545301.2878298083 J/mol` + +Calculating the properties of a reaction given as a reaction equation (calcite dissolution): + +```python + # T(K) P(Pa) reaction equation + R = engine.thermoPropertiesReaction(348.15, 1e5, "Calcite = Ca+2 + CO3-2") + print(f'drS of (Cal = Ca+2 + CO3-2) is {R.reaction_entropy.val}') + print(f'drG of (Cal = Ca+2 + CO3-2) is {R.reaction_gibbs_energy.val}') + print(f'logK of (Cal = Ca+2 + CO3-2) is {R.log_equilibrium_constant.val}') +``` + +``` +drS of (Cal = Ca+2 + CO3-2) is -259.12288450418015 +drG of (Cal = Ca+2 + CO3-2) is 59914.092580924975 +logK of (Cal = Ca+2 + CO3-2) is -8.988976334909019 +``` + +Using the batch class to do sequential calculations and output the results to a CSV file: + +```python + batch = fun.ThermoBatch(database) + batch.setPropertiesUnits(["temperature", "pressure"],["degC","bar"]) + batch.setPressureIncrement(0,0,0) + batch.setTemperatureIncrement(0,300, 5) + substances = ["Na+", "Mg+2", "Ca+2", "SiO2@"] + properties = ["heat_capacity_cp","entropy", "volume"] + batch.thermoPropertiesSubstance(substances, properties).toCSV('results.csv') +``` + +Jupyter notebooks with examples on how to use `ThermoFun` can be found here [https://github.com/thermohub/thermofun-jupyter](https://github.com/thermohub/thermofun-jupyter). More complex scripts and routines can be coded that call `ThermoFun`. For example, `ThermoFun` can be coupled to a phase diagram plotter, a chemical equilibrium solver that require properties of all components in the system, or a parameter optimization routine for fitting the parameters of the methods available in ThermoFun. + +# Acknowledgements + +# References diff --git a/paper/paper.pdf b/paper/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7f537211d685356a1d8389ba02b513de7d1968f2 GIT binary patch literal 258706 zcmaI7b9AI%6fGDV9otFAwr$($I33%zZL?$Bwr$&XGWC1!t(mvh%)I)izE!nu)%|Xr zea_zdoFY>Y5u;_KV}&7`TOwqWo3ZNSI0rV&XiCdaC_^3Tnc?qNuQu27T8MnktLV z_Gma%+8{*;kY%|b@YONfL! z2n-_-D}Zw0+Ss_Pe)^gFQLgS_ObrR`5>1#QCP#h%@=Ii#J`Q(-xgK%rN}7>{1PUKX%OF26;_FvFd;t6Oit=P+LmC$Wi5g)r z4SUVf0kY!=&ZVCkK0lyA^^rP8ol&a=^E8hlLCty&c2`cP*I`HwkGUUeg#$unwZ<-$ zF^Un1*oxGOJ~H!hucmaT@(B4e<=E`%?9yy+uvUov&B-K)FFuMPt!ROsk()a+o?KL1 z+p0;txV@Ad4I!GhmQbnUA7{&1rTtSXrR3wU!uT%ric6UrT7p{OkBp!{D1M+}o+m(I 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paper/compile_paper.sh | 5 + paper/examples.ipynb | 20 +- paper/figure1.png | Bin 215824 -> 456569 bytes paper/functions.py | 87 ++++++++ paper/paper.ipynb | 330 +++++++++++++++++++++++++++++ paper/paper.jats | 466 ----------------------------------------- paper/paper.md | 76 ++++--- paper/paper.pdf | Bin 258706 -> 0 bytes 8 files changed, 476 insertions(+), 508 deletions(-) create mode 100755 paper/compile_paper.sh create mode 100644 paper/functions.py create mode 100644 paper/paper.ipynb delete mode 100644 paper/paper.jats delete mode 100644 paper/paper.pdf diff --git a/paper/compile_paper.sh b/paper/compile_paper.sh new file mode 100755 index 0000000..0b90f44 --- /dev/null +++ b/paper/compile_paper.sh @@ -0,0 +1,5 @@ +docker run --rm \ + --volume $PWD:/data \ + --user $(id -u):$(id -g) \ + --env JOURNAL=joss \ + openjournals/inara diff --git a/paper/examples.ipynb b/paper/examples.ipynb index 1fa019d..9d55ad3 100644 --- a/paper/examples.ipynb +++ b/paper/examples.ipynb @@ -12,18 +12,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "13b94f94", "metadata": {}, "outputs": [], "source": [ - "database = fun.Database(\"aq17-thermofun.json\")\n", + "database = fun.Database('aq17-thermofun.json')\n", "engine = fun.ThermoEngine(database)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "id": "f0c85f6e", "metadata": {}, "outputs": [ @@ -37,13 +37,13 @@ ], "source": [ "# T(K) P(Pa) symbol\n", - "Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, \"Ca+2\")\n", + "Ca_ion = engine.thermoPropertiesSubstance(473, 2000e5, 'Ca+2')\n", "print(f'G0 {Ca_ion.gibbs_energy.val} J/mol')" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "id": "a36c08e6", "metadata": {}, "outputs": [ @@ -59,7 +59,7 @@ ], "source": [ "# T(K) P(Pa) reaction equation\n", - "R = engine.thermoPropertiesReaction(348.15, 1e5, \"Calcite = Ca+2 + CO3-2\")\n", + "R = engine.thermoPropertiesReaction(348.15, 1e5, 'Calcite = Ca+2 + CO3-2')\n", "print(f'drS of (Cal = Ca+2 + CO3-2) is {R.reaction_entropy.val}')\n", "print(f'drG of (Cal = Ca+2 + CO3-2) is {R.reaction_gibbs_energy.val}')\n", "print(f'logK of (Cal = Ca+2 + CO3-2) is {R.log_equilibrium_constant.val}')" @@ -67,17 +67,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "717d85a2", "metadata": {}, "outputs": [], "source": [ "batch = fun.ThermoBatch(database)\n", - "batch.setPropertiesUnits([\"temperature\", \"pressure\"],[\"degC\",\"bar\"])\n", + "batch.setPropertiesUnits(['temperature', 'pressure'],['degC','bar'])\n", "batch.setPressureIncrement(0,0,0)\n", "batch.setTemperatureIncrement(0,300, 5)\n", - "substances = [\"Na+\", \"Mg+2\", \"Ca+2\", \"SiO2@\"]\n", - "properties = [\"heat_capacity_cp\",\"entropy\", \"volume\"]\n", + "substances = ['Na+', 'Mg+2', 'Ca+2', 'SiO2@']\n", + "properties = ['heat_capacity_cp','entropy', 'volume']\n", "batch.thermoPropertiesSubstance(substances, properties).toCSV('results.csv')" ] }, diff --git a/paper/figure1.png b/paper/figure1.png index 8efb75df37a9a9e5c814bacc5dc00b3194864969..76cef88376d5dcd8006846d4f5c752a3a5f7da48 100644 GIT binary patch literal 456569 zcmeFY1yfwz5;huwhM*z1dx8gd3GVK0!F6zVcMXKW3GRct%i!)#aCg6x_tdHTp1SuJ zeDzJu)X3h`yH~I7XRq#lR=9$kI5Hw0;=6b6kR>HVl-|9AWqkMUeKrCduG=(2?Z!gDXbx` zJx~js1K@r8Yg<~sXYK-nUNAX|5l)EVzn+R?Mf)#S|M~%P zP_csPVu}f==l^|1IfjR){?GpmlcZ%@S^s?wQl5^{R>8kz8{+JK!eB)HmmH*?FR4G> zEdJ+mVoklh(*JV~(=a8K9^U_4PLbi;|5fS#Xe;rGayssW-2wmOK=4PzIMmRk>i;;w z=e^WxR2oi!`nA5V-7$nbJCTg-ap&juXITHZjhrYVBI3?4ZDmqAo0Ufcj@W_Gi`+j) zvRNO2RC<};qlg9cLXh$QyqNs;y#C^ml_o%e*?8ufe28MR%ugmENN^FHQU6DpsgFg+ zbT3#rY$%Z$Jdq<@@I4rD3S^=DqOBFDW@}qPbNKDsw|bo~y#%~&3g9*)(JL5q#G*4? zTwJ9lN7L}Iu&PHQvM+bf&t5iGM#jeFE5b0l8JU?Crh4h)$mC2(&Od(qRw+@DBqJx6 zE@)5uVQ83Ry#$gJVAQA*Y<4**IYZ6^r7@dEWH|KWYMqs>KP-1Y0&{{NZC}PFgqyTG zSgtiXYOS-&z@Go8iJ;yZ6%mn8Rz`O=H)RY?p|5e+7QqxsR*f>O%hom7|0-1FW45TU_Vkav 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