This library make the process of fitting highly accurate thermodynamic mixture models much easier, facilitating their use by a large swath of the community. A modular Python-based library is developed, with methods for a number of the common operations (loading of data files, adding additional helper data, building a cost function, plotting results, etc.). The user is required to define the cost function, and an example showing how that can be achieved is presented.

This is a python package to perform thermodynamic extrapolation andinterpolation of observables calculated from molecular simulations. This allowsfor more efficient use of simulation data for calculating how observables changewith simulation conditions, including temperature, density, pressure, chemicalpotential, or force field parameters.

The ThermoData Engine fully implements all major principles of the concept of dynamic data evaluation formulated at NIST?s Thermodynamics Research Center (TRC). Dynamic data evaluation relies on large electronic databases to store the currently known experimental data along with detailed descriptions of relevant metadata and uncertainties. Combining this data with expert software allows the ThermoData Engine to producing data compilations 'to order' which include all major thermophysical properties (such as density, vapor pressure, heat capacity, enthalpies of phase transitions, critical properties, melting and boiling points, etc). As the name of this resource implies, its scope is only pure compounds. For binary mixtures please see NIST ThermoData Engine - Pure Compounds and Binary Mixtures - SRD 103b (https://www.nist.gov/srd/nist-standard-reference-database-103b).

ThermoML is an XML-based IUPAC standard for the storage and exchange of experimental thermophysical and thermochemical property data. The ThermoML archive is a subset of Thermodynamics Research Center (TRC) data holdings corresponding to cooperation between NIST TRC and five journals: Journal of Chemical Engineering and Data (ISSN: 1520-5134), The Journal of Chemical Thermodynamics (ISSN: 1096-3626), Fluid Phase Equilibria (ISSN: 0378-3812), Thermochimica Acta (ISSN: 0040-6031), and International Journal of Thermophysics (ISSN: 1572-9567). Data from initial cooperation (around 2003) through the 2019 calendar year are included. The original scope of the archive has been expanded to include JSON files. The JSON files are structured according to the ThermoML.xsd (available below) and rendered from the same experimental thermophysical and thermochemical property data reported in the corresponding articles as the ThermoML files. In fact, the ThermoML files are generated from the JSON files to keep the information in sync. The JSON files may contain additional information not supported by the ThermoML schema. For example, each JSON file contains the md5 checksum on the ThermoML file (THERMOML_MD5_CHECKSUM) that may be used to validate the ThermoML download. This data.nist.gov resource provides a .tgz file download containing the JSON and ThermoML files for each version of the archive. Data from initial cooperation (around 2003) through the 2019 calendar year are provided below (ThermoML.v2020-09.30.tgz). The date of the extraction from TRC databases, as specified in the dateCit field of the xml files, are 2020-09-29 and 2020-09-30. The .tgz file contains a directory tree that maps to the DOI prefix/suffix of the entries; e.g. unzipping the .tgz file creates a directory for each of the prefixes ( 10.1007, 10.1016, and 10.1021) that contains all the .json and .xml files. The data and other information throughout this digital resource (including the website, API, JSON, and ThermoML files) have been carefully extracted from the original articles by NIST/TRC personnel. Neither the Journal publisher, nor its editors, nor NIST/TRC warrant or represent, expressly or implied, the correctness or accuracy of the content of information contained throughout this digital resource, nor its fitness for any use or for any purpose, nor can they, or will they, accept any liability or responsibility whatever for the consequences of its use or misuse by anyone. In any individual case of application, the respective user must check the correctness by consulting other relevant sources of information.

teqp is a library written in C++ that allows for calculation of numerical derivatives of thermodynamic equations of state with respect to the independent variables with numerical differentiation tools. No hand-written derivatives are required, and the computational speed is competitive with, or better than, the results from hand-written derivatives in low-level programming languages.

This data includes a four alchemical processes with data files generated with the python package: generate_alchemical_lammps (DOI from MIDAS) and the resulting output to be used for the calculation of free energies using Multi-state Bennett Acceptance Ratio (MBAR), BAR, or Thermodynamic Integration (TI). These input files are only applicable for LAMMPS versions after April 2024. The four cases can be separated into two systems, benzene solvated in water, and a Lennard-Jones (LJ) dimer in solvent. These four cases are:1) benzene 1: In the NPT ensemble, scale the charges of benzene atoms from full values to zero over six steps.2) benzene 2: In the NPT ensemble, scale the van der Waals potential between benzene and water from full values to zero over sixteen steps.3) benzene 3: In the NVT ensemble with benzene in vacuum, scale the charges of benzene's atoms from zero to full values over six steps.4) lj_dimer: In the NPT ensemble, change the cross interaction energy parameter between solvent and dimer from a value of 1 to 2.

This dataset includes the python scripts and output results for fitting SAFT models to the phase equilibrium density, pressure, and speed of sound for refrigerants. A conda environment file is included specifying the dependencies, and the driver script does all the fitting. The process is carried out in parallel through the use of process-level parallelism. The output files are in the JSON format, in a hierarchical folder data structure. Summary files are generated in the csv format. A README file is included within the zip file.