Computational Chemical Biology research cluster
University of North Texas Regents Professor of Chemistry Angela Wilson is featured in “Trailblazers of North American Research,” the September 2012 issue of International Innovation, a leading global journal and open access resource for the wider scientific, technology and research communities. The profile centers on Wilson’s research into the thermodynamic properties of the periodic table, and the use of computational chemistry models to quantify and predict the behavior of molecules.
The computational chemistry program at UNT is one of the largest and well-respected programs of its kind in the world. Wilson and team integrate chemistry with cutting-edge theory and simulation, experiment, and characterization. Investigations tackle a range of problems from the atomic to the continuum scale involving proteins, gaseous molecules, catalysts, and biopolymers, with applications in materials science, transition metal chemistry, and environmental chemistry.
Computing periodicity: a model challenge
Firstly, what led you to investigate the thermodynamic properties of the periodic table?
Thermodynamic properties serve as a critical gauge for the performance of any computational chemistry methodology. They are less forgiving than many other properties, as simple error cancellation is less likely to occur in the calculation of properties such as enthalpies of formation. Knowing that a methodology can properly predict enthalpies (a measure of the total energy of a thermodynamic system) is a good indicator that the methodology is more likely to be effective in determining other energetic properties. Thermochemical properties are also of great importance in understanding reactivity.
What are the main aims and objectives of your research?
Some of the main aims and objectives of my research are to extend the quantitative predictive powers of computational chemistry to molecules of increasing size, and to do so with similar levels of accuracy that are possible for smaller molecules. To accomplish this, one challenge is to address the computational cost (the amount of time, memory, and disk space) required in quantum mechanical ab initio calculations. Whilst this is a challenge facing all computational scientists, the improvement of computer technology does help this to some extent. Of course, the production of large amounts of additional data by ever more powerful systems is a problem in and of itself.