Additional Resources

The modules you've completed serve as an immersive introduction into one of the most profound quests in human history—the search for extraterrestrial life. Picture a universe brimming with boundless possibilities, where distant planets may cradle the enigmatic secrets of life beyond Earth. As you've discovered, scientists employ state-of-the-art technology, such as powerful telescopes and sophisticated space probes, to meticulously scrutinise our cosmic neighbours, on a quest for clues that might unveil the existence of alien life forms. The pursuit of comprehending our place in the cosmos has reached unprecedented levels of excitement, propelling us into uncharted territories, fuelled by the tantalising prospect that we may not be solitary inhabitants in the vastness of space!

To deepen your understanding of this captivating subject, this page offers additional resources. These resources will provide you with a more profound insight into the search for extraterrestrial life, research related to the origins of life and astronomy, the pivotal role of computational chemistry in detecting biosignatures on exoplanets, and guidance on creating visually appealing content. Happy exploring!

Computational Chemistry

Computational Chemistry is a very broad area of chemistry that has been expanding significantly over the last couple of decades. Computational chemistry can be used to predict the vibrational frequencies of molecules to generate approximate infrared spectrum of these molecules. It is beyond the scope of this depth study, but as part of the UNSW SciX program, students ran computational chemistry simulations using the freely-available IQMol software to predict infrared spectra of potential biosignatures. We have reproduced some of the teaching content below.

Introduction

Paul Dirac once said, “The fundamental laws necessary for the mathematical treatment of a large part of physics and the whole of chemistry are thus completely known, and the difficulty lies only in the fact that application of these laws leads to equations that are too complex to be solved.”

Computational chemistry aims to solve these equations through approximations and assumptions that will allow us to understand the results.

By the end of this page, you should be able to:

• Understand how Computational simulations benefit chemistry and the search for biosignatures.

• Discuss some of the approximations that go into Quantum Chemistry and the role of Quantum Theory

• Explain what is meant by Level of Theory and Basis Set

• Know what a Scaling Factor is and why it is used

Jacobs Ladder and Zeta Quality

Within Computational chemistry the most important requirements for a calculation are: a system or molecule, the level of theory, and the basis set. Level of theory and Basis sets have size, and generally the bigger the more accurate, but also slower in calculation time. Because we are using Density Functional Theory for our calculations, we can explain the level of theory with Jacobs Ladder. While not a perfect relationship we can see that as you increase the complexity of your level of theory you increase your accuracy. This is also similar for Basis Sets. The size (or Zeta Quality) of your basis set will affect your accuracy. There are different types of Basis Set Families and some of these are optimised for specific applications. Some of the main basis set families are: Pople, Dunning, and Jensen.

Basis Set: a set of mathematical functions used to explain the positions of the electrons within your system. The shape and size of your basis set will affect the accuracy of your calculation.

Level of Theory: The manner in which you solve the Schrodinger Equation. This includes the approximations you are taking into account, and this will affect the accuracy of your calculations.

Scaling Factors

“Pure” density functionals usually give accurate vibrational frequencies due to an error cancellation resulting from the neglect of anharmonicity and compensating deviations from the true harmonic frequency (101). Hybrid functionals, on the other hand, exhibit an improved accuracy for the calculation of harmonic frequencies. However, if the effect of anharmonicity is taken into account, frequency calculations with hybrid functionals suffer from a systematic error compared to experimental values. One way to overcome this deficiency is to use scaling factors that shift uniformly the computed frequencies so that they are closer to experimental values.

There are scaling factors for each combination of level of theory and basis set, which need to be included in your evaluation of vibrational spectra. 

Trying Computational Chemistry Yourself

The great advantage of these programs is that once the system, level of theory and basis set are selected the program does all of the calculations for you! 

• Download here 

• User guide here: 

Data analysis can be greatly assisted with the Python and Jupyter notebooks for our data analysis. Download Anaconda for Python 3.xxx for your platform here 

This is a good resource for learning important skills in python. It is a full guided day worth of work, and will go into more detail than needed, but is very helpful for understanding python. The first 9 sections will be of most use. 

To compare your calculated spectra to (if available) experimental data we will be using the NIST webbook . You may want to consider this when selecting your research question. 

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