Perspective on coarse-graining, cognitive load, and materials simulation
Perspective on coarse-graining, cognitive load, and materials simulation
复制标题
DOI：
10.1016/j.commatsci.2019.109129
复制
发表时间：
2020-01
影响因子：
3.3
通讯作者：
Eric Jankowski;Neale Ellyson;Jenny W Fothergill;Michael M. Henry;Mitchell H. Leibowitz;Evan D Miller;Mone’t Alberts;Samantha Chesser;Jaime D. Guevara;Chris D. Jones;M. Klopfenstein;Kendra K. Noneman;Rachel Singleton;Ramon A. Uriarte-Mendoza;Stephen Thomas;Carla E. Estridge;Matthew L Jones
中科院分区：
文献类型：
--
作者：
Eric Jankowski;Neale Ellyson;Jenny W Fothergill;Michael M. Henry;Mitchell H. Leibowitz;Evan D Miller;Mone’t Alberts;Samantha Chesser;Jaime D. Guevara;Chris D. Jones;M. Klopfenstein;Kendra K. Noneman;Rachel Singleton;Ramon A. Uriarte-Mendoza;Stephen Thomas;Carla E. Estridge;Matthew L Jones
今天，计算材料科学的预测能力源于模拟工具、建模技术和最佳实践的重叠进步。我们通过解释这些领域的重要贡献如何相互作用来概述分子模拟的生态系统。这些工具、技术和实践的组合输出是研究人员通过有效地将简单模型与功能强大的软件相结合来提高理解的能力。作为具体的例子，我们展示了有机光伏形态的预测在过去十年中如何改进了数量级，以及如何现在可以用百万粒子模型来研究反应环氧热固性的过程。我们从认知负荷理论的角度讨论了这两个材料系统以及材料模拟器的培养。对于学生来说，生态系统组成的广泛视角应该有助于理解关键部分是如何相互联系的，然后才是有针对性的探索。通过这种方式，本文以类似于粗粒度模型的松散方式进行组织：主要组件提供基本框架和加速采样，从中更好地结合更深层次的研究。对于导师来说，这篇文章的组织是为了及时提供当前模拟生态系统的快照，并为模拟专家提供进入教学实践文献的入口。
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.