CAREER: Rational Design of Ferroelectric Semiconductors

非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，以加速发现一类稀有的半导体并促进对其物理性质的理解。半导体，如硅，具有绝缘体和金属之间的导电性，是用于信息处理、太阳能电池和发光二极管的电子设备的基本组件。典型的电子器件包括同一半导体材料的交替层之间的一个或多个界面，其中添加了额外的带正电荷或带负电荷的杂质。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。这种半导体，凭借其原子结构，有一个内置的电场，可以帮助电荷在它们之间移动。此外，在这种半导体中，电荷流动的方向可以通过外部电场来改变。通过消除接口，这类半导体有望实现更有效和更高效的设备，应用于能源生成和存储、信息存储和处理等。该项目的教育和推广活动涉及使用增强现实(AR)在原子水平生动而清晰地展示复杂的材料结构和动态过程。这些活动将在本科生和研究生课程中作为单元实施，以促进学生对复杂材料结构的理解，并增强结构与性质的相关性。AR数据集将通过在线储存库传播，以便在其他机构广泛使用，激励学生进入材料科学领域。技术总结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，铁电半导体是一种罕见的电子材料，将半导体和铁电材料的特性结合在一种材料中。这项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相结合的方法来预测新的、稳定的铁电半导体，具有在任何一类材料中都无法获得的广泛性质。那些被预测具有最有希望的一组性质的铁电半导体将被合成，并通过合作来表征它们的性质。随后，将结合电子显微镜和密度泛函理论计算来评估微观结构和组成对功能性能的影响。这个项目的教育目标是让学生参与、激发和教育学生通过了解材料的原子结构来理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)的各种物质结构和动力学过程的原子模型来实现，并将其作为有效解释结构-性质关联的模块进行传播。这些模块将在本科生和研究生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。

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.