The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be investigated with million-particle models. We discuss these two materials systems and the training of materials simulators through the lens of cognitive load theory.
For students, the broad view of ecosystem components should facilitate understanding how the key parts relate to each other first, followed by targeted exploration. In this way, the paper is organized in loose analogy to a coarse-grained model: The main components provide basic framing and accelerated sampling from which deeper research is better contextualized. For mentors, this paper is organized to provide a snapshot in time of the current simulation ecosystem and an on-ramp for simulation experts into the literature on pedagogical practice.
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Jankowski, Eric; Ellyson, Nealee; Fothergill, Jenny W.; Henry, Michael M.; Leibowitz, Mitchell H.; Miller, Evan D.; Alberts, Mone't; Guevara, Jamie D.; Jones, Chris D.; Klopfenstein, Mia; Noneman, Kendra K.; Singleton, Rachel; and Jones, Matthew L. (2020). "Perspective on Coarse-Graining, Cognitive Load, and Materials Simulation". Computational Materials Science, 17, 1109129-1 - 109129-11. https://dx.doi.org/10.1016/j.commatsci.2019.109129