CAREER: Unlocking Ductility in Magnesium: How to Replace Twinning and Impede Damage

非技术总结这个职业奖支持旨在使镁成为更有用的材料的基础研究。目前，镁合金对于许多应用来说太脆，尽管比钢或铝更轻且更强。脆性较小的镁将广泛用于制造更轻、更高效的车辆。在这个项目中，PI将使用人工智能方法开发更好的镁合金计算模型，并使用这些模型在原子尺度上研究裂纹的起源。从这些模拟中获得的见解将用于制定策略，通过改变合金元素的配方和用于制造镁零件的温度来降低镁的脆性。该奖项还支持PI在本科和研究生阶段的教育活动。除了在计算建模和机器学习领域培训学生外，还将开发一种新的教育软件，通过将成熟的计算工具和校准代码与简单的图形界面相结合，具有强大的材料特性探索和发现研究能力。PI开发了一个分层课程，让本科生和研究生都参与计算建模技术。本课程将通过提供先进的建模工具，使更多的人能够理解这些工具，并将其作为材料研究的拓展工具，激励学生研究尚未有人回答的问题。技术总结本职业奖支持旨在揭示镁缺乏延展性的机制的基础研究活动。到目前为止，研究人员还没有很好地理解镁中裂纹是如何形成的，以及如何防止它们。诸如加工硬化和合金化的缓解策略仅显示出有限的成功，这阻止了镁实现广泛的市场用途。这在很大程度上是由于镁的塑性各向异性，激活所需的非Schmid应力，以及复杂的活性塑性模式的多样性，如c + a slipp.Using分子动力学和拓扑modeling，镁的变形和破坏将研究通过使用各向同性近似插入位错和孪晶到模拟箱和表征反应在塑性变形。由于镁的塑性模式的行为受到合金元素的严重影响，模拟包括在固溶体中的重要添加剂，如铝，锌，钇和铋也将被采用。为了有效地做到这一点，这些固溶体二元合金的新的原子间相互作用势将开发使用快速原子神经网络方法。因此，收缩孪晶的作用及其与其他孪晶、位错、溶质和晶界的相互作用将被量化。这将使PI能够开发增强韧性机制的策略，从而延缓裂纹的形成。该奖项还支持PI在本科和研究生阶段的教育活动。除了在计算建模和机器学习领域培训学生外，还将开发一种新的教育软件，通过将成熟的计算工具和校准代码与简单的图形界面相结合，具有强大的材料特性探索和发现研究能力。PI开发了一个分层课程，让本科生和研究生都参与计算建模技术。通过使先进的建模工具可供更广泛的受众使用和理解，这门课将作为材料研究的外展工具，激励学生研究尚未有人回答的问题。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

NONTECHNICAL SUMMARYThis CAREER award supports basic research designed to make magnesium a more useful material. Currently, magnesium alloys are too brittle for many applications, despite being lighter and stronger than steel or aluminum. Less brittle magnesium would be broadly useful to make lighter and more efficient vehicles. In this project, the PI will develop better computational models for magnesium alloys using artificial intelligence methods and use these models to investigate the origins of cracks at the atomic scale. The insights gathered from characterizing these simulations will be used to develop strategies for making magnesium less brittle by changing the recipe for alloying elements and temperatures used to create magnesium parts.The award also supports the PI’s educational activities at the undergraduate and graduate levels. In addition to training students in the areas of computational modeling and machine learning, a new educational software will be developed with powerful research capabilities for material property exploration and discovery by coupling well established computational tools and calibration code with a simple graphical interface. The PI has developed a split-level course to engage both undergraduates and graduate students in computational modeling techniques. By making advanced modeling tools available and understandable to a broader audience, this class will serve as an outreach tool for materials research, inspiring students to study questions that no one has yet answered.TECHNICAL SUMMARYThis CAREER award supports basic research activities designed to reveal mechanisms producing the lack of ductility of magnesium. A good understanding of how cracks form in magnesium and how to prevent them has eluded researchers so far. Mitigation strategies such as work hardening and alloying have only shown limited success which has prevented magnesium from achieving broad market use. This is largely due to magnesium’s plastic anisotropy, non-Schmid stresses required for activation, and the diversity of complex active plastic modes such as c+a slip.Using molecular dynamics and topological modeling, magnesium deformation and failure will be studied by using the isotropic approximation to insert dislocations and twins into the simulation box and characterizing reactions during plastic deformation. Since the behaviors of Mg’s plastic modes are critically affected by alloying elements, simulations including important additives in solid solution such as aluminum, zinc, yttrium, and bismuth will also be employed. To do this effectively, new interatomic potentials for these solid solution binary alloys will be developed using the rapid atomistic neural network method. As a result, the role of contraction twins and their interactions with other twins, dislocations, solutes, and grain boundaries will be quantified. This will enable the PI to develop strategies which enhance ductile mechanisms and thereby retard crack formation.The award also supports the PI’s educational activities at the undergraduate and graduate levels. In addition to training students in the areas of computational modeling and machine learning, a new educational software will be developed with powerful research capabilities for material property exploration and discovery by coupling well established computational tools and calibration code with a simple graphical interface. The PI has developed a split-level course to engage both undergraduates and graduate students in computational modeling techniques. By making advanced modeling tools available and understandable to a broader audience, this class will serve as an outreach tool for materials research, inspiring students to study questions that no one has yet answered.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.