DMREF: Collaborative Research: Machine learning exploration of atomic heterostructures towards perfect light absorber and giant piezoelectricity

摘要非技术描述：最近发现的原子薄的二维(2D)材料，如石墨烯、过渡金属二卤化物、黑磷等，为通过垂直堆积(即2D异质结构)控制原子级材料开辟了新的机会。二维异质结构材料设计的艺术正处于萌芽阶段，由于我们可以通过实验获得的二维单分子层的势能库达到约1000个，仅N个单分子层的最低能量堆积就会导致1000N个可能的异质结构。我们的计划寻求通过新的机器学习(ML)指导的密度泛函理论(DFT)对有用的2D异质结构的发现过程产生转换影响，例如可见光光谱中的完美光吸收和巨型压电性。我们组建了一个由数据科学和ML的应用、2D材料及其光学性质的建模、2D异质结构的分子束外延生长以及材料和器件表征方面的专家组成的团队。这些新型设计型2D异质结构的成功展示将开启一个高效和有目的的材料设计方法学的新时代。该计划计划为所有参与小组制定一个三管齐下的扩大参与计划，包括本科生研究、社区推广和暑期计划。技术描述：二维原子晶体的研究最近集中在它们的异质结构上，这一新兴领域的进步已经导致了超导和磁学等引人入胜的发现。然而，成千上万种不同的二维层状材料及其排列几乎是无限的异质结构组合。这项研究将开发一种新颖的ML引导的DFT框架，结合物理激励的原子描述符，应用数据科学来寻找具有目标特性的设计者异质结构。作为概念验证，我们将通过优化填充和空带之间的能带嵌套来展示具有完美光吸收的异质结，以及通过设计电负性偶极矩来展示巨型压电性。这些具有目标性质的异质结构将用超清洁的最先进的分子束外延方法生长，并表征它们的吸收和压电系数。然后，实验和理论之间的证实将指导对所提出的ML和DFT模型以及总体策略的可能改进。这些新的设计者2D异质结构的成功展示将开启一个高效和有目的的材料设计方法的新时代。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

AbstractNontechnical Description: Recent discovery of atomically thin two-dimensional (2D) materials, such as graphene, transition metal dichalcogenides, black phosphorus, among many others, has opened new opportunities to atomic level materials control through vertical stacking, i.e. 2D heterostructure. The art of 2D heterostructure materials design is at its nascent stage, and with the potential library of 2D monolayers that we can access experimentally amounting to about 1000, just the mere lowest energy stacking of N monolayers would then lead to 1000N possible heterostructures. Our program seeks a transformational impact on the discovery process of useful 2D heterostructures, such as perfect light absorption in the visible spectrum and giant piezoelectricity, through novel machine-learning (ML)-guided density functional theory (DFT). We have assembled a team comprising of experts in data science and the application of ML, modeling of 2D materials and their optical properties, molecular beam epitaxial growth of 2D heterostructures, and materials and device characterization. The successful demonstration of these new designer 2D heterostructures would usher in a new era of efficient and purposeful materials design methodology. A three-pronged broadening participation program for all participating groups in this proposal is planned, which includes undergraduate research, community outreach, and summer programs.Technical Description: Research in two-dimensional atomic crystals has recently focused on their heterostructures, and the advancements in this emerging field has already led to fascinating discoveries such as superconductivity and magnetism. However, thousands of different 2D layered materials and their permutations amount to almost infinite heterostructure combinations. This research will develop a novel ML-guided DFT framework, in conjunction with physically motivated atomistic descriptors, which applies data science in the search for designer heterostructures with targeted properties. As a proof-of-concept, we will demonstrate heterostructures with perfect light absorption through optimizing the band nesting between the filled and empty bands as well as giant piezoelectricity through engineering the electronegativity dipole moments. These heterostructures identified with the targeted properties will be grown with ultra-clean state-of-the-art MBE approaches, and their absorption and piezoelectric coefficients characterized. Corroboration between experiments and theory will then instruct on possible improvements to the proposed ML and DFT models and overall strategy. The successful demonstration of these new designer 2D heterostructures would usher in a new era of efficient and purposeful materials design methodology.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.