Designing Structural Alloys Through Interphase Boundary Segregation Engineering

结构合金对于基础设施和能源部门至关重要，并在汽车，航空航天和核工业中有应用。这些合金系统通常是添加了某些杂质原子以提高其机械强度和热稳定性的金属材料。提高机械强度最常用的策略之一，称为沉淀硬化，依赖于添加形成化合物的元素，并赋予优异的高温强度。虽然是一种成功的策略，但这些合金可以使用的温度范围是有限的。特别地，在高温下，强度在部件的寿命期间降低。该奖项支持有助于提高沉淀硬化结构合金的工作温度和热稳定性的研究。关于这些合金在加工过程中的行为的新科学知识将使高性能材料的设计成为可能。该研究方法结合了基于计算模拟的工具和机器学习算法，并专注于基于轻质镁的模型合金系统，该系统具有提高汽车行业能源效率的潜力。因此，该项目的更广泛的影响结合联合收割机在结构材料的商业进步，在培训中学教师和研究生参与project.This工作的教育效益的范围侧重于开发一种新的自动化框架，用于模拟相间边界使用机器学习的方法来计算原子间的潜力。研究人员将使用三元Mg-Sn-Zn合金来验证这一框架，但该方法可以推广到任何二元合金系统中筛选潜在的三元溶质系统。这种能力对于设计具有前所未有的热稳定性的沉淀系统至关重要。该奖项还为代表性不足群体的学生提供研究机会，并培训下一代中学教师。来自北卡罗来纳州教育部的学生将通过机器学习和原子模拟主题的合作研究项目参与科学过程。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Structural alloys are crucial for the infrastructure and energy sectors, and have applications in the automotive, aerospace and nuclear industries. These alloy systems are usually metallic materials with certain impurity atoms added to improve their mechanical strength and thermal stability. One of the most commonly used strategy of improving mechanical strength, called precipitation hardening, relies on adding elements that form compounds, and impart excellent high-temperature strength. While a successful strategy, the temperature range at which these alloys can be utilized is limited. In particular, at high-temperatures, the strength degrades during the life-time of the component. This award supports research that will help increase the operating temperatures and thermal stability of precipitation-hardened structural alloys. New scientific knowledge about the behavior of these alloys during processing will enable the design of high-performance materials. The research approach combines computational simulation-based tools and machine-learning algorithms, and focuses on a model alloy system based on lightweight Magnesium, which has the potential to improve energy efficiency in the automotive industry. The broader impacts of this project thus combine commercial advances in structural materials, with a range of educational benefits in training secondary school teachers and graduate students involved with the project.This work focuses on the development of a novel automated framework for simulating interphase boundaries using a machine learning approach to calculate interatomic potentials. The researchers will use the ternary Mg-Sn-Zn alloy for validating this framework but the approach can be generalized for screening potential ternary solute systems in any binary alloy system. This capability is crucial for designing precipitating systems with unprecedented thermal stability. This award also provides research opportunities to students from underrepresented groups, and the training of the next generation of secondary school teachers. Students from the NC State Education Department will participate in the scientific process through collaborative research projects on the themes of machine learning and atomistic simulations.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.