Published: Sep 27, 2013 by Gavin
One of the members of my current research group is defending her master’s thesis next week and as I was reading through her thesis, I was reminded of my favourite part of my own thesis. In the introductory chapter, I wrote a brief description of the development of the modern field of computational chemistry. More than a year later, these four paragraphs remain some of my favourite writing to date, so I wanted I’d share it with the world here.
The ability to predict chemical phenomena and reaction outcomes using mathematical rules was a goal of many early chemists. Some of the foremost chemists of the late 18th and early 19th centuries, like Antoine Lavoisier, John Dalton, Joseph Gay-Lussac, and Amedeo Avogadro, worked to study chemistry in a more quantitative fashion than their predecessors, and in fact many of their contemporaries. Gay-Lussac was optimistic that their work would come to fruition when he wrote, in a contribution to Mémoires de Physique et de Chimie de la Société d’Arcueil, in 1809, “…we are perhaps not far removed from the time when we shall be able to submit the bulk of chemical phenomena to calculation.” Through the 19th century, work on thermodynamics and chemical kinetics, much of which was built on the work of Gay-Lussac and his colleagues, led to a new branch of chemistry known as physical chemistry, which applied the concepts of physics to the study of chemistry.
Although physical chemistry introduced new levels of quantitative study to the world of chemistry, it was primarily focused on analysing phenomena in the bulk. With the advent of both atomic and quantum theory, it became desirable, and indeed possible, to study chemistry on the molecular and atomic scales. The work of Erwin Schrödinger introduced a mathematical model for the interactions of electrons and nuclei in atomic systems and marked the beginning of modern electronic structure theory. Unfortunately, the solutions for Schrödinger’s equation become exceptionally complex for large systems, so it took some time before his work could be realistically applied to the vast majority of chemical problems. Through the mid to late twentieth century, the development of theoretical methods that allowed chemists to solve accurate approximations of the Schrödinger equation for large molecular systems, in conjunction with rapid advances in computing power, led to the introduction of computational chemistry. This work was acknowledged with the 1998 Nobel prize in chemistry awarded to Walter Kohn and John Pople for their contributions to the development of density-functional theory and computational methods in quantum chemistry, respectively.
In the early days of computational chemistry, electronic structure calculations on a single diatomic molecule could constitute an entire doctoral thesis; however, with today’s advances in the theoretical methods and computing power available, these same calculations can be performed on a desktop computer in seconds and have been incorporated into undergraduate chemistry curricula. Highly accurate calculations on systems containing a few dozen atoms have become routine in chemistry research and the maximum size of systems that can be studied is constantly increasing.
Computational chemistry provides a new way to study chemistry; in the view of some, it achieves the lofty vision of Gay-Lussac. With computational chemistry, one can model discrete molecules or infinitely repeating materials and learn about specific interactions in chemical systems that can be very difficult, or even impossible, to study using traditional experimental chemistry techniques. It is particularly useful for the study of chemicals that are dangerous or simply malodorous, as is the case for the compounds in the work presented here, as one can learn a great deal without needing to come in physical contact with them. Computational studies are increasingly used in chemical research to probe a chemical process of interest quickly before expending resources to study potentially unfavourable reactions, synthesising expensive compounds, or investigating unexpected experimental results.