Collaborative Research: GOALI: Experimentally-Validated Computational Approach to Developing and Predicting Kinetics in Anisotropic Systems

非技术性总结：减轻车辆重量可以提高燃油效率，从而抵消能源开采和使用的经济和社会成本上升。镁作为钢和铝的潜在替代物是有吸引力的，因为其低密度和高刚度/重量和强度/重量比。然而，合金开发可能是一个缓慢且昂贵的过程。 为了加快新型铸造合金的设计，本研究使用实验验证的计算方法，特别强调采用动力学方法。本着材料基因组计划的精神，该项目汇集了计算和理论（伊利诺伊大学香槟分校），实验（佛罗里达大学）和工业建模（ThermoCalc，LLC），共同努力开发科学和数据，以预测加工，材料结构和最终性能之间的关系。随着研究凝固和预测原子在原子尺度上如何运动的新方法的发展，科学和工程的进步将适用于其他合金。该项目利用了计算能力的新进展，新的理论发展和新的高通量实验方法。该项目将支持两名博士生，教师参与本科生研究和高中科学教师的教育。技术总结：为了减少排放并提高运输车辆的燃油经济性，人们已经做出了相当大的努力来了解和增强镁合金的结构-性能关系，因为它们的密度低。这个合作项目将计算（伊利诺伊大学香槟分校）和实验（佛罗里达大学）以综合的方式结合起来，以显着提高铸造镁合金的运输知识。本着材料基因组计划的精神，PI以综合和自洽的方式进行，以了解和预测凝固过程中的传输现象。该方法集成了计算和实验，提供验证和连接的基本机制中尺度传输。与ThermoCalc，LLC合作，他们将创建新的动力学数据库，以实现材料开发。这种方法的目标是在预测新型镁合金相变现象的框架内对镁中的溶质和空位输运产生新的基本理解。对多尺度行为的统一理解为材料基因组框架中的未来合金开发提供了一种科学驱动的方法。该计划的数据将通过公共数据库（NIST MatDL）和中尺度相场和连续级模拟数据库的开发进行传播。通过考虑温度、时间和成分的影响，本研究的预测结果可以完全融合工艺-结构和结构-性能预测结果。该项目有四个相互依赖的任务来预测铸造镁的介观微观结构：1）确定HCP和液态镁在二元和高阶体系中的迁移率。密度泛函理论（DFT）将通过计算活化能和迁移率来引导这种方法。2)通过实验和DFT中的分子动力学模拟确定所有相关系统的固液表面能和摩尔体积。3)在储存库和迁移率数据库中收集摩尔体积、表面能和迁移率。DICTRA模拟扩散倍数相比，偏析行为和均匀化温度与实验数据验证。4)通过迁移率、摩尔体积和表面能的积分预测凝固微观结构。模拟包括预测作为温度和冷却速率的函数的核数密度、尺寸和组成，并与从类似处理路线获得的实验晶粒尺寸分布进行验证。预期的结果是对HCP金属中原子输运的新的基本理解，体积和表面能数据库，将作为一个储存库和基础，增加系统的复杂性（额外的阶段，溶质等）。数据将以逻辑、可管理和可访问的方式提供，以便过渡到商业实践。该平台可以允许在未来的其他相关阶段的成核行为的预测。

Non-technical summary:Reducing the weight of vehicles can increase fuel efficiency, which counters the rising economic and social costs of energy extraction and use. Magnesium is attractive as a potential alternative to steel and aluminum because of its low density, and high stiffness/weight and strength/weight ratios. However, alloy development can be a slow and costly process. To expedite the design of new casting alloys, this research uses experimentally validated computational methods, with particular emphasis on enabling a kinetically-informed approach. In the spirit of the Materials Genome Initiative, this project brings together computation and theory (Univ. Illinois, Urbana-Champaign), experimentation (Univ. Florida), and industrial modeling (ThermoCalc, LLC) in a joint effort to develop the science and data to predict relationships between processing, material structure, and eventually properties. The science and engineering advances will be applicable to other alloys as new methods are developed to study solidification and predict how atoms move at the atomic scale. This project takes advantage of new advances in computing power, new theoretical developments, and new high-throughput experimental methods. The program will support two PhD students, and the faculty are involved both in undergraduate research and education of high-school science teachers.Technical summary:To combat emissions and increase the fuel economy in transportation vehicles, considerable effort has been made to understand and enhance the structure-property relationships in magnesium (Mg) alloys due to their low density. This collaborative project unites computation (Univ. Illinois, Urbana-Champaign) and experimentation (Univ. Florida) in an integrated fashion to significantly advance the knowledge of transport in cast Mg alloys. In the spirit of the Materials Genome Initiative, the PIs proceed in an integrated and self-consistent manner to understand and predict transport phenomena during solidification. The approach integrates computation and experiment throughout, providing validation and connecting fundamental mechanisms to mesoscale transport. In collaboration with ThermoCalc, LLC, they will create new kinetic databases to enable material development.The goal of this approach is to produce a new fundamental understanding of solute and vacancy transport in Mg in a framework to predict phase transformation phenomena for new Mg alloys. A unified understanding of multiscale behavior provides a science-driven approach to future alloy development in a materials-genome framework. The data from this program will be disseminated via public databases (NIST MatDL) and the development of databases for mesoscale phase-field and continuum-level simulations. By accounting for temperature, time and compositional effects, the predictions from this work allow for the complete merger of processing-structure and structure-property predictions.The project has four tasks that build upon each other to predict the mesoscale microstructure for cast Mg: 1) Determine mobilities in binary and higher order systems for HCP and liquid Mg. Density functional theory (DFT) will lead this approach by calculating activation energies and mobilities. 2) Determine solid-liquid surface energies and molar volumes in all relevant systems via experiments and molecular dynamics simulations in DFT. 3) Assemble molar volumes, surface energies and mobilities in repositories and mobility databases. DICTRA simulations are compared to diffusion multiples, and segregation behavior and homogenization temperatures validated with experimental data. 4) Predict solidified microstructures through the integration of mobilities, molar volumes and surface energies. Simulations include the prediction of nuclei number density, size, and composition as a function of temperature and cooling rate, and are validated with experimental grain size distributions obtained from similar processing routes.The expected outcomes are a new fundamental understanding of atomic transport in HCP metals, with mobility, volumetric and surface energy databases that will serve both as a repository and a foundation for increasing system complexity (additional phases, solutes, etc.). The data will be supplied in a logical, manageable, and accessible fashion for transition into commercial practice. This platform may allow predictions of nucleation behavior in other relevant phases in the future.