A chance to reconsider our scientific workforce

Man with computer

Source: © M-H Jeeves

The future chemist will have the power to dive deep into experimental, theoretical and computations chemistry

The way we perceive and approach the world around us has been revolutionised by computers in such a short period of time, reshaping our cognitive processes, and altering our fundamental perspectives. Isn’t it also obvious to assume a huge change in the way we do our very own jobs, as chemists? I am not referring to typical changes such as processing our data quicker or searching the web for research articles in just a minute. Hidden deeper is the integration of computers into chemical science itself. But is this a transformative tsunami that’s going to alter things fundamentally, or merely an exaggerated ripple in a pond?

During the last two decades computational chemistry has transitioned from an elitist activity done by a few, to an important investigative tool – or even a strict requisite for a publication. Be it as a guide for synthesis, or a way to shed light and understand physicochemical data, computers are the main protagonists and not the theory itself; remember that DFT and the other commonly used theories were established many decades ago. Nevertheless, it was not until the advent of unprecedented computational power that a true change of perspective came. The use of different levels of theories (from molecular mechanics to ab initio and DFT) surged in experimental papers and now theory is more than ever a guide for doing experiments. Modern computers have given us the luxury of fast, reliable and sound output data, such that they are already taken for granted (although of course, that assumes the input is equally sound).

There is a trend for undergraduate curricula to include computational chemistry as an essential part of the study programme. And yet, many chemists appear almost speechless in the face of the developments in computer science and technology, biding the time for some computer-savvy magician to bring the new tools and teach us how to use them blindly.

Rather crudely, there are the experimentalists, dealing with synthesis and characterisation, theoreticians who develop the theory, and computational chemists who implement it. Although one can learn basic computational skills and focus on synthesis (or the opposite), modern specialisation often leaves chemists with an insufficiently deep understanding of areas outside of their main field. To take a phrase from quantum chemistry, what seems to be missing is someone who truly represents the concept of ‘insight and numbers’; a combination of accurate computation and chemical understanding.

Experimentalists are attracted to insight; empirical ways to digest knowledge and explain phenomena. On the other hand, theoreticians favour numbers, seeking accuracy and deeper meanings to phenomena. We can imagine the transfer of knowledge as a line of points: From theoreticians to computational chemists and then to experimentalists. And behold! A gap emerges due to miscommunications and mistranslations down the line.

It’s time for an update. Chemist v2.0, to coin the term, will help to connect all the previous points in the chemist v1.0 line.

One chemist to rule them all

I reckon the perfect place for chemist v2.0 to be is a spectroscopic lab that has a close connection with a theoretical group. They can synthesise the presumed compound, study it with spectroscopical methods, and then use theory and computations to shed light on the problem.

Chemist v2.0 knows how to do the experiment and works on the theory involved with it as well: a precious translator for each group, able to understand what everyone wants and does. Being a chemist v2.0 is not about just learning to press keys and use computers as a black box. Understanding coding itself and mastering the theory behind software is crucial, while also making contributions on the experimental part. One chemist to rule them all.

On top of that, artificial intelligence is already knocking on the door of many chemical disciplines, showing great potential for the advancement of our field. Machine learning and all the powerful tools that are under the umbrella of AI will alter the way we do and realise chemistry in the future, giving chemist v2.0 the opportunity to shine as they have the potential to ‘tame’ such technologies. Having the background to criticise and critique them returns us back to the proper combination of fundamental computer knowledge, theory and practical chemistry.

Even though a scenario like this seems science-fiction or even heretic, it will probably be mainstream in the next decade or so, and we will unavoidably be part of it. The key is to find the correct balance of chemistry and computing skills, leading finally to the concept of chemist v2.0. And while not everyone may have the ability to combine theory and practice in such a way, future chemists (at least those who do not wish to see their work become obsolete) will have to recruit such a chemist to their group for, if nothing else, avoiding the precarious pitfalls of modern research.