Collaborative Research: CDS&E: Charge-density based ML framework for efficient exploration and property predictions in the large phase space of concentrated materials

合作研究：CDS

• 批准号: 2302764
• 负责人: Pejman Tahmasebi
• 金额: $ 30.38万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302764&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& Charge; density

Non-technical summaryFuture engineering applications require complex materials to withstand extreme environments. Electronic structure calculations have played an integral role in developing fundamental understanding of atomic and electronic level properties of materials. To tackle the challenges of growing materials complexities, in this project, the investigator at Clemson University will collaborate with investigators at Colorado School of Mines to integrate data-science based image-recognition techniques with electronic structure calculations to predict materials properties. Image recognition is widely used for face recognition, lane-assisted driving, food-contaminant detection, cancer-cell detection, etc. In this project, the charge density of materials will be used in the form of images to learn the electronic structure of materials to enable property predictions in complex materials. The project will contribute to technical, educational and workforce development. A fundamental understanding of lattice distortion in complex alloys will be delivered, namely in high entropy alloys that consist of multiple principal elements in large concentrations. The project will develop an open-source machine learning framework with a curated database of charge densities and alloys’ properties. It will also train undergraduate and graduate students for future digital economy at the intersection of materials physics and data science via a new ‘data science in materials science’ course, and summer workshops.Technical summaryThe chemical randomness in high entropy alloys engenders unique nearest neighbor environments causing lattice and electronic distortions that result in large uncertainties in properties both qualitatively and quantitively. The uncertainties scale with compositional (atomic fraction) and chemical (different elements) diversities resulting in an extremely stiff challenge for density functional theory (DFT) to explore the phase space. This technical challenge runs parallel to the scientific challenge of mechanistic reasons of composition-property correlations. Since, charge density is the fundamental quantity from which the physics and property correlations can be extracted, the investigators will develop a charge-density based machine learning framework that will elucidate the role of disruptive energy landscape on the emerging properties, and simultaneously remove the bottleneck to trace the large phase. The machine learning models will learn the charge density distributions and properties from simpler alloys and predict them in complex alloys while bypassing expensive DFT calculations altogether. The investigators will work under the hypothesis that larger asymmetry in charge density leads to larger uncertainty in properties. The students will learn to perform electronic structure calculations, data generation and interpretation, and application of machine learning models to predict materials properties. The students will also learn image-recognition techniques applied to materials science problems. Summer workshops will be organized by the investigators to engage girls in STEM with an interactive, engaging and hands-on approach. The investigators will also organize a virtual workshop with a specific focus on feature recognition techniques for materials predictions.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.

非技术摘要工程应用需要复杂的材料来承受极端环境。电子结构计算在发展对材料的原子和电子水平特性的基本理解中起着不可或缺的作用。为了应对不断增长的材料复杂性的挑战，在这个项目中，克莱姆森大学的研究人员将与科罗拉多州矿业学院的研究人员合作，以将基于数据科学的图像识别技术与电子结构计算相结合以预测材料属性。图像识别被广泛用于面部识别，车道辅助驾驶，食品 - 抗抑制剂检测，癌细胞检测等。在该项目中，将以图像的形式使用材料的电荷密度来学习材料的电子结构，以在复杂的材料中启用财产预测。该项目将有助于技术，教育和劳动力发展。对复杂合金中晶格失真的基本理解将被传递，即在高熵的高熵合金中，这些合金由多个主要元素组成。该项目将使用策划的电荷密度和合金属性的数据库开发开源机器学习框架。它还将通过新的“材料科学的数据科学”课程和夏季研讨会的新的材料物理和数据科学的交集来培训本科生和研究生，以实现材料物理和数据科学的交汇。不确定性量表具有组成（原子分数）和化学（不同的元素）多样性，从而对密度功能理论（DFT）产生了极度艰巨的挑战，以探索相空间。这项技术挑战与组成特性相关性的机械原因的科学挑战平行。由于电荷密度是可以提取物理和性质相关性的基本数量，因此研究人员将开发基于电荷密度的机器学习框架，该框架将阐明颠覆性能量景观在新兴属性上的作用，并简单地去除瓶颈以追踪大相。机器学习模型将从更简单的合金中学习电荷密度分布和属性，并以复杂的合金预测它们，同时绕过昂贵的DFT计算。研究者将在以下假设下起作用，即较大的电荷密度会导致性质的不确定性较大。学生将学习执行电子结构计算，数据生成和解释以及机器学习模型以预测材料属性。学生还将学习适用于材料科学问题的图像识别技术。调查人员将组织夏季研讨会，以互动，引人入胜且动手的方法使女孩参与STEM。调查人员还将组织一个虚拟研讨会，特别关注材料预测的功能识别技术。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估来获得的支持。