Walking down computational chemistry memory lane one acronym at a time

Abstract

History is often thought to be dull and boring – where large numbers of facts are memorized for passing exams. But the past informs both the present and future; particularly in delineating the context surrounding specific events that, in turn, help provide us with a deeper understanding of their underlying causes and potential implications. To the uninitiated, the computational chemistry literature appears intimating given the pervasive use of acronyms (each with specialist meaning) and eponymous method names. While jargons expedite communication of complex ideas between practitioners in the field, and add clarity to a discussion (e.g., explaining complicated concepts in plain language may not capture subtle - but important - nuances in meaning), they nevertheless presents a significant barrier to understanding for researchers in other fields. Specifically, an inability to comprehend the meaning of the various terms and jargons used would significantly impede understanding and navigation of the literature – and may translate into difficulty in selecting appropriate tools for the task at hand. Scientific progress – both incremental and breakthroughs - is built upon prior work. By placing various computational methods and techniques along a chronological thread, a commentary article aims to demystify the tangled web of acronyms and terms that populate the electronic structure calculations literature and highlights the interrelationships between methods – particularly, how one method evolved from another. Additionally, the chronological framework also allows readers to appreciate developments in computational chemistry through the lens 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 charting progress in the field. Finally, the chronological time-line delineated also provides an opportune backdrop for examining the perennial question of whether computational power (both capacity and speed) or theoretical insights play a more important role in advancing computational chemistry research. Particularly, while availability of large amount of computational power at declining cost and advent of graphics processing unit (GPU) powered parallel computing are enabling tools for solving hitherto intractable problems; 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 article should be useful as a primer for researchers to gain a more holistic understanding of computational chemistry, and students wishing to learn more about the conceptual basis and purpose of various electronic structure calculations methods prior to venturing into the field’s expansive literature.