Dr Benyamin Motevalli1, Dr Amanda Barnard1
1CSIRO Data61, Docklands, Australia
The energies obtained using first principles methods can be used to study thermodynamic stabilities of complex systems, particularly where experimental measurements are limited due to difficulties. However, the calculated energies only account for the ground state (temperature T ≈ 0K, pressure P=0 Pa) electronic energies, E. One practical solution to extend the ground state energies to finite temperatures and pressures is the first principles (ab initio) thermodynamics method, which combines the results calculated from first principles at the ground state, and the extensive thermochemical data measured at the standard state. This method can also serve as an effective technique to expand datasets in a more sensible way by calculating the probabilities as a function of temperature, pressure, and other environmental conditions such as humidity.
QuickThermo is a software package that enables such calculations. It has a user-friendly interface which is developed in C#, using WPF technology and has a proper database developed in SQLite. The database also includes a number of predefined elements with corresponding measured thermochemical data. This database is flexible and can be grown by users. The interface provides convenient tools to define elements and structures and calculate thermodynamic probability for various environmental conditions such as temperature, pressure, and humidity. Further, the software provides batch run capabilities, where users can load any number of structures and perform the calculations for a range of environmental conditions. Also, a range of interactive plots are embedded in the software to display some results.
Dr. Benyamin Motevalli is a Postdoctoral Fellow in Data61 at CSIRO. He has years of experience in developing/employing computational and numerical analysis techniques to establish fundamental understanding of novel intelligent nanomaterials. His current research focuses on rational design of materials through innovative data-driven models that offer the advantage of fusing complex experimental and computational data for a higher-level understanding of structure-processing-property relationships