What drives computational chemistry forward: theory or computational power?
- Published
- Accepted
- Subject Areas
- Biochemistry, Bioinformatics, Biophysics, Computational Biology, Computational Science
- Keywords
- science history, simulation, approximations, assumptions, theory, ab initio, electronic calculations, experiment, semi-empirical, first principles calculations
- Copyright
- © 2015 Ng
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. What drives computational chemistry forward: theory or computational power? PeerJ PrePrints 3:e552v3 https://doi.org/10.7287/peerj.preprints.552v3
Abstract
History is often thought to be dull and boring – where large numbers of facts are memorized for passing exams. But the past informs the present and future; particularly in delineating the context surrounding specific events that, in turn, help provide a deeper understanding of their underlying causes and implications. Scientific progress (whether incremental or breakthroughs) is built upon prior work. Chronological examination of the field’s evolution reveals the existence of major “epochs” (e.g., transition from semi-empirical methods to first-principles calculations), and the centrality of key ideas (e.g., Schrodinger equation and Born-Oppenheimer approximation) in potentiating progress in the field. The longstanding question of whether computing power (both capacity and speed) or theoretical insights play a more important role in advancing computational chemistry was examined by taking into account the field’s development holistically. Particularly, availability of large amount of computing power at declining cost, and advent of graphics processing unit (GPU) powered parallel computing are enabling tools for solving hitherto intractable problems. Nevertheless, the article argues, using Born-Oppenheimer approximation as an example, that theoretical insights’ role in unlocking problems through simple (but insightful) assumptions is often overlooked. Collectively, the essay should be useful as a primer for appreciating major development periods in computational chemistry, from which, counterfactual questions illuminate the relative importance of intuition and advances in computing in moving the field forward.
Author Comment
This is a synopsis article that summarizes a longer manuscript. This version updates manuscript title, abstract and content.