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

非技术综述：减轻车辆重量可以提高燃料效率，这抵消了能源开采和使用不断上升的经济和社会成本。镁的密度低、刚性/重量比和强度/重量比高，是钢和铝的潜在替代品，具有很大的吸引力。然而，合金的开发可能是一个缓慢而昂贵的过程。为了加快新铸造合金的设计，这项研究使用了经过实验验证的计算方法，特别强调实现了一种动态知情的方法。本着材料基因组倡议的精神，该项目将计算和理论(Univ.伊利诺伊州，厄巴纳-香槟)，实验(大学(佛罗里达州)和工业建模(ThermoCalc，LLC)，共同努力发展科学和数据，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移动的新方法的发展，科学和工程上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。该计划将支持两名博士生，教职员工参与本科生研究和高中理科教师的教育。技术摘要：为了减少排放并提高交通工具的燃油经济性，人们做出了相当大的努力来了解和加强镁合金的结构-性能关系，因为镁合金的密度很低。该协作项目将计算(Uni.伊利诺伊州，厄巴纳-香槟)和实验(大学以一种综合的方式，显著提高对铸造镁合金的运输知识。本着材料基因组倡议的精神，PI以一种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。该方法在整个过程中集成了计算和实验，提供了验证并将基本机制与中尺度传输联系起来。他们将与ThermoCalc，LLC合作，创建新的动力学数据库来实现材料开发。这种方法的目标是在一个框架内对镁中的溶质和空位迁移有一个新的基本理解，以预测新的镁合金的相变现象。对多尺度行为的统一理解为材料基因组框架中未来的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)和开发中尺度相场和连续谱水平模拟的数据库进行传播。通过考虑温度、时间和成分的影响，这项工作的预测允许完全合并工艺-结构和结构-性能预测。该项目有四个相互建立的任务来预测铸态镁的介观微观结构：1)确定HCP和液态镁在二元系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，确定了所有相关体系的固液表面能和摩尔体积。3)将摩尔体积、表面能和迁移率聚集在储存库和迁移率数据库中。将DICTRA模拟与扩散倍数、偏析行为和均一化温度进行了比较，并与实验数据进行了验证。4)结合迁移率、摩尔体积和表面能预测凝固组织。模拟包括作为温度和冷却速度的函数的核数密度、大小和成分的预测，并用从类似的处理路线获得的实验晶粒度分布进行验证。预期的结果是对HCP金属中原子输运的新的基本理解，具有迁移率、体积和表面能数据库，这些数据库将作为存储库和增加系统复杂性(额外的相、溶质等)的基础。数据将以合乎逻辑、可管理和可访问的方式提供，以过渡到商业实践。这一平台可能允许在未来的其他相关阶段预测成核行为。

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.