Thermodynamic data for chemical reactions deals with the energy change in accordance with the laws of thermodynamics. This represents macroscopic properties of the system, that is the initial and final states not involving the mechanism. It is of great benefit to understand thermodynamic properties of chemical reactions for the development of chemical reactions theories and industrial practice. Alfa Chemistry provides calculation of thermodynamic data for individual chemical reactions including prediction of the limit, direction, and energy change of chemical reactions. We have prepared a variety of services such as environmental protection, materials science, and life science for you.
Our Thermodynamic Data Calculation Services
Chemical research
Calculate the influence of reaction conditions, such as temperature, pressure, supercritical fluids, ionic liquids and so on. Predict the reaction limit, reaction direction, and energy change (such as enthalpy, Gibbs energy, and internal energy), and help to achieve the target reaction and strengthen the theoretical research.
Life science
The main research object of life science is large molecules such as protein and DNA, which can be efficiently analyzed by thermodynamic calculation. Screening drug crystal structures and analysis of drug candidates with calculated thermodynamics data accelerate drug discovery and development.
Materials science
Thermodynamics is an integral part of the field of materials science and engineering. Successful material and process design requires reliable thermodynamic data. The effects of degree of dispersion on thermodynamical properties, reaction directions, and chemical equilibria, and nanoparticle size on thermodynamic properties of chemical reactions can be calculated.
Industrial manufacture
Taking full advantage of raw materials and energy is the target of industrial manufacture. Calculation of thermodynamic data for corresponding chemical reactions is desirable and helpful to optimize the process, reduce the cost, and increase production.
Environmental protection
Scientists use the calculation of thermodynamic data to reduce entropy increase, improving energy efficiency and protect the environment. Our calculation of thermodynamic data is conducive to the increase of energy utilization, achievement of energy conservation and pollution reduction.
Our Workflow for Thermodynamic Data Calculation
We provide raw data and analysis results according to your specific requirements.
Computational Strategies for Thermodynamic Data
Ab initio methods
We apply computational ab initio methods combined with the group contribution methods and group additive values to obtain thermodynamic properties of larger molecules with near chemical accuracy. Our scientists can use Ab initio calculation methods to obtain heats of formation and Gibbs free energies. In addition, we can also use ab initio molecular dynamics methods, which combine density functional theory with molecular dynamics techniques, to provide accurate predictions of the limit and direction for individual chemical reactions.
First-principles calculations
At Alfa Chemistry, we can perform the calculations of thermodynamic parameters based on first-principles methods together with the quasi-harmonic approximation. Heat capacity at constant volume and constant pressure, entropy, thermal expansion coefficient, isothermal bulk modulus, adiabatic bulk modulus are able to obtained. Moreover, our teams apply first-principles calculations to phase diagrams and to calculate various thermodynamic data, such as the formation enthalpy of compounds, enthalpy of mixing, heat of phase transformation, binding energy, etc.
Machine learning
We use machine learning prediction procedure to perform thermodynamic calculation with our database to reduce calculation time. The microstructure can be evaluated accurately by using machine learning parameter. In addition, our thermodynamics calculation consists of obtaining Gibbs free energy and chemical potential that are varied as functions of temperature and composition. Alfa Chemistry’ machine learning technique can be applicable to simulate non-equilibrium phase transformation of additive manufacturing condition with high numerical stabilization.
Hartree-Fock methods
Hartree-Fock methods are widely used to estimate thermodynamic properties and usually give better agreement with experimental data. Our groups use Hartree-Fock approaches to calculate various thermodynamic properties such as entropy, heat capacity of gases, for the interpretation of molecular spectra, calculations of bond length, bond angles, dipole moment and the under-standing of intermolecular forces.
CALPHAD method.
As a powerful tool to calculate phase diagrams, CALPHAD is employed to model thermodynamic properties for each phase and simulate multicomponent multi-phase behavior in complex systems. We perform CALPHAD for the calculation of thermodynamic quantities and simulation of phase transformations from free energy as a function of temperature, composition and pressure. Other properties such as atomic mobility, molar volume, thermal conductivity and diffusivity, viscosity and surface tension of liquids, electrical resistivity can also be obtained using our CALPHAD technology.
Analysis data
- Corresponding energy data of specific reactions.
- Possible side reactions.
- Optimized energy consumption of reactions.
- Influence factors in reactions.
- … and more.
Key Features
- Calculation with optimal algorithms
- Complete characteristic data of chemical reactions
- A wide range of services prepared for your project
- High-quality analysis results
- Fast turnaround
Alfa Chemistry is dedicated to the calculation of thermodynamic data for individual chemical reactions, advancing the development of chemical theories, materials, life science, and industry. Our services of calculation of thermodynamic data will promote your innovative research and resolve problems about chemical reactions. Please feel free to contact us for assistance in your project.
Reference
- Liu, Z.K. Computational thermodynamics and its applications. Acta Mater. 2020, 200: 745-792.