JThermodynamicsCloud is the third generation of the development of the calculation of temperature dependent thermodynamic properties from 2D-graphical (Lewis structures) representations. The implementation calculates thermodynamics of radical species based on three fundamental techniques, THERGAS, Benson Radical Rules and THERM. All of these methods are based on structure additivity rules, meaning that the thermodyanamic contributions of local structures can be added together to the final total temperature dependent These methods differ primarily on how they calculate radical species. The temperature dependent thermodynamics of the parent molecule are calculated in the same way. The symmetry of the molecule and radicals are determined by JThermodynamics based on the methods and data of THERGAS and Benson's book.
Group Additivity: The basis of the calculation is the additivity method devised by Benson in Thermochemical Kinetics: Methods for the Estimation of Thermochemical Data and Rate Parameters. The basis of the method is that the temperature dependent thermodynamics of a molecular species can be calculated by adding (additivity rules) up structural features. The library of these structural features constitute the database from which the calculation is performed.
THERGAS: This is a direct implementation of the Benson's method from 1995 for radical species. THERGAS calculates radicals based on the difference between the parent molecule (hydrogen added to radical) and the radical,including the disassociation energy. The changes in symmetry, vibrational moments and rotational energies are found and calculated (these effect both the entropy and the heat capacity contributions). Energetic contributions due to disassociation energies and steric energies are taken into account relative to the parent molecule.
THERM: Ritter and Bozelli implemented the Hydrogen Bond Increment with the THERM method (THermo Estimation for Radicals and Molecules), and extension of the Benson additivity rule method to radical species. It differs from THERGAS in that it has a single structure which accounts for the differences between the radical and the parent molecule (the radical with the hydrogen added): 'Properties for radical and biradical species are calculated by applying bond dissociation increments to a stable parent molecule to reflect loss of H atom'.
JTHERGAS: JTHERGAS is a re-implementation of the THERGAS method, whose goal was make changes to the fundamental data more flexible and the fundamental data itself more transparent (visible). This was done by translating each correction into a 2D-graphical substructure (as defined by the Chemical Development Toolkit, CDK), which is recognized with the species to be calculated, and, once recognized, a corresponding correction. New corrections could be easily added by creating new substructures and their corresponding values through input files or the web interface. The use of these substructures transfers all updates to the mySQL database and not to the platform independent (programmed in JAVA) implementation. The major advance was the correction representation (2D-graphical, Lewis structures) and the use of the database (and the web interface) to clearly delineate the implementation from the data used for the calculation.
JThermodynamics: JThermodynamics is the second generation implementation of JTHERGAS, using the same methodology, and to add the Hydrogen Bond Increment (HBI) structures and methodology to the mySQL-based database. The software technical advance of this implementation was to further debug and cleanup the methodology and to create a stand-alone platform independent application based on command lines at the terminal that could be easily downloaded and installed on the users local computer. JThermodynamics has been used to further advance the database of fundamental data.
ThermodynamicsCloud: JThermodynamicsCloud is the third generation of development built upon the JThermodynamics implementation. Its primary advancements are putting the implementation, including the database in a SaaS Cloud service and the extensive use of ontologies to describe all data objects within the system and to drive the system. In addition, all the data