CAREER: Rational Design of Ferroelectric Semiconductors

该奖项支持与教育相结合的理论，计算和实验研究，以加速发现一类罕见的半导体并促进对其物理特性的理解。硅等半导体的导电性介于绝缘体和金属之间，是信息处理、太阳能电池、发光二极管等电子设备的重要组成部分。典型的电子器件包括在相同半导体材料的交替层之间的一个或多个界面，附加的带正电荷或带负电荷的杂质已经被添加到所述界面。这些接口经常降低电子设备的效率。该项目旨在开发一类无需创建此类接口即可运行的半导体。这样的半导体，由于它们的原子结构，有一个内置的电场，可以帮助它们移动电荷。此外，这种半导体中电荷流动的方向可以通过外部电场来切换。通过消除界面，这类半导体有望实现更有效、更高效的设备，应用于能源生产和存储、信息存储和处理等领域。本项目的教育和推广活动包括使用增强现实（AR）生动、清晰地展示原子水平上复杂的材料结构和动力学过程。这些活动将作为本科生和研究生课程的模块实施，以启动学生对复杂材料结构的理解，并增强结构-性能相关性的保留。AR数据集将通过在线存储库传播，以便在其他机构广泛使用，激发学生进入材料科学领域。技术概述该奖项支持与教育相结合的理论，计算和实验研究，以加速铁电半导体的发展，这是一种罕见的电子材料，将半导体和铁电体的特性联合收割机结合在一种材料中。该研究将采用第一原理密度泛函理论计算，群论方法和材料信息学的组合来预测新的，稳定的铁电半导体，其具有各种各样的特性，这些特性在任何一类材料中都无法获得。那些被预测具有最有前途的性能的铁电半导体将被合成，并通过合作表征其性能。随后，微观结构和组合物的功能特性的作用将使用电子显微镜和密度泛函理论计算的组合进行评估。该项目的教育目标是通过了解材料的原子结构来吸引，激发和教育学生了解和预测材料的性质。这一目标将通过开发基于增强现实（AR）的各种材料结构和动力学过程的原子模型，并将其作为有效解释结构-性能相关性的模块进行传播来实现。这些模块将在本科和研究生阶段的各种材料科学课程中实施，并将在网上提供给其他机构使用。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 SUMMARY This award supports theoretical, computational, and experimental research integrated with education to accelerate the discovery of a rare class of semiconductors and advance the understanding of their physical properties. Semiconductors, such as silicon, have conductivity between that of insulators and metals and are essential components of electronic devices used for information processing, solar cells, and light-emitting diodes. Typical electronic devices include one or more interfaces between alternating layers of the same semiconductor material to which either additional positively or negatively charged impurities have been added. These interfaces often reduce the efficiency of the electronic devices. This project aims to develop a class of semiconductors that can operate without the need for creating such interfaces. Such semiconductors, by virtue of their atomic structure, have a built-in electric field that can help move charges across them. Moreover, the direction of the flow of charges within such semiconductors can be switched by an external electric field. By eliminating the interfaces, this class of semiconductors is expected to enable the realization of significantly more effective and efficient devices with applications in energy generation and storage, information storage and processing, and others.The education and outreach activities of this project involve using Augmented Reality (AR) to vividly and clearly illustrate complex material structures and dynamical processes at the atomic level. These activities will be implemented as modules in undergraduate and graduate curriculum to jump-start students’ understanding of complex material structures and enhance retention of structure-property correlations. AR datasets will be disseminated through an online repository to enable widespread use at other institutions, energizing students about entering the field of materials science.TECHNICAL SUMMARYThis award supports theoretical, computational and experimental research integrated with education to accelerate the development of ferroelectric semiconductors, a rare class of electronic materials that combine the properties of both semiconductors and ferroelectrics in a single material. The research will employ a combination of first-principles density-functional-theory calculations, group-theoretical methods, and materials informatics to predict new, stable, ferroelectric semiconductors with a wide range of properties that are inaccessible in either of the individual classes of materials. Those ferroelectric semiconductors that are predicted to have the most promising set of properties will be synthesized and their properties characterized through collaborations. Subsequently, the role of microstructure and composition on the functional properties will be evaluated using a combination of electron microscopy and density functional theory calculations. The education goal of this project is to engage, excite, and educate students about understanding and predicting properties of materials by knowing their atomic structure. This objective will be achieved through the development of augmented-reality (AR)-based atomic models of various material structures and dynamical processes, and their dissemination as modules to efficiently explain structure-property correlations. These modules will be implemented in various Materials Science courses at both the undergraduate and graduate levels, and will be made available online for use at other institutions.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.