DMREF: Collaborative Research: Structure Genome of Metal-Insulator Transitions

非技术描述：世界上的计算能力有了巨大的增长，但目前半导体行业的前进道路充满了障碍。未来一代电子器件的不同策略是基于存在于多种电子状态的材料。为此，需要新一代的电子材料，以及在多个电子状态之间切换的手段。基础科学是这个项目的重点，集中在某些材料的电学性质的突然变化，当他们通过所谓的金属到绝缘体的转换由外部触发。在过渡的一边，这种材料表现得像铜金属，而在另一边，它表现得像绝缘木材。该项目的目标是设计和发现具有金属到绝缘体转变的材料，使其能够在室温下操作，并在感兴趣的关键特性上显示出巨大的变化；电阻率。该策略是通过原子尺度的结构设计来控制性能。该方法采用了实验、理论和文献数据挖掘的紧密结合，这将使新的见解出现，并有助于设计理想的材料。该项目将提供一套研究工作流程，用于评估和实验验证新概念，以发现关键材料。该项目将阐明选择高性能材料的协议，从而扩大可能影响未来技术的化合物的调色板。学生的教学和培训与项目的发现能力是相互交织的，旨在通过调查人员及其小组成员参与公共宣传活动来扩大参与。本科和研究生课程模块的开发以及学生在跨学科团队环境中的参与是项目计划的内在内容。该项目将在一系列氧化物和新的计算材料方法上产生大量新的和挖掘的数据。这些信息将被汇集到公共门户网站的开放数据库中。技术描述：该项目将通过建立单位细胞级晶体结构和宏观电子响应之间的联系的方法，从理论、数据和综合实验中获益，追求发现材料的原子水平遗传密码，显示金属到绝缘体的转变。目前，破译金属到绝缘体过渡的遗传密码（一般描述符）的基本数据和结构-电子功能关系并不存在一种允许预测合成的格式。这个项目吗?它的重要意义在于，它将这个问题重新定位为原子结构的问题，重点关注不同类型的结构扭曲所起的作用，特别是呼吸模式、扬-泰勒扭曲，以及在广泛的结构类型和化学过程中的类似佩尔斯的不稳定性。该项目将生成和收集一系列数据，这些数据将允许将电子交互映射为原子特征，应用基于信息学的方法来实现监督和无监督学习。该项目将阐明选择高性能材料的预测规则和协议，从而扩大化合物的调色板，从而影响电子技术以外的技术。多个层次的学生教学和培训与项目的发现能力相互交织，旨在通过公共宣传活动，通过开发本科和研究生课程模块来扩大参与；最后，让学生参与到跨学科的团队环境中。该项目将产生关于一系列氧化物和新的计算材料方法的大量新数据。这些数据将使用一种为材料数据设计的新的便携式文件格式汇集到公共门户网站的数据库中。数据可视化的新方法将允许外部用户交互、查询和分析数据，以实现本文所提出的目标之外的目标。用于生成金属到绝缘体性能定量模型的数据驱动模型和信息学工作流程将与上述数据和可视化工具一起托管在MIST：金属和绝缘体结构调谐平台上。pi还计划将MIST作为开源发布，并通过确保感兴趣的研究人员能够为MIST代码库做出贡献，围绕该平台建立一个用户社区。这将使该项目得到更广泛的发展。高级网络基础设施办公室的软件集群对这方面特别感兴趣，该办公室为该奖项提供了共同资金。合成、理论和表征方面的进步将通过允许学生、学术或工业研究人员将制定的结构-性质关系用于教育和研究目的，从而加强科学能力和劳动力。

Non-technical Description: The world has seen an enormous increase in computing power, but the current path forward for the semiconductor industry is beset with roadblocks. A different strategy for a future generation of electronic devices is based on materials that exist in multiple electronic states. A new generation of electronic materials are required for this purpose, as are the means for switching between multiple electronic states. The fundamental science that is the focus of this project is centered on the sudden change in the electrical properties of certain materials when they are switched through a so-called metal-to-insulator transition by an external trigger. On one side of the transition, the material behaves like copper metal, while on the other side, it behaves like insulating wood. The project goal is to design and discover materials exhibiting such metal-to-insulator transitions that enable room-temperature operation and that display large changes in the key property of interest; the electrical resistivity. The strategy is to control properties by structural design at the atomic scale. The approach employs a tightly integrated combination of experiment, theory, and data-mining of the literature, that would enable new insights to emerge and aid in the design of desirable materials. This project will deliver a research workflow with a suite of tools to enable assessment and experimental validation of new concepts for the discovery of key materials. The project will articulate protocols for selecting high-performing materials, leading to an expanded palette of compounds that could impact future technologies. The teaching and training of students and the discovery capabilities of the project are interwoven, and aimed at broadening participation through the involvement of the investigators and their group members in public outreach events. The development of modules for undergraduate and graduate courses and the involvement of students in interdisciplinary team environments are intrinsic to project plan. The project will yield a plethora of new and mined data on a range of oxides and new computational materials approaches. These will be aggregated into open-access databases on public portals.Technical description: This project will pursue discovery of the atomic-level genetic code of materials displaying metal-to-insulator transitions through approaches that establish links between unit cell level crystal structure and the macroscopic electronic response, profiting from a coupling of theory, data, and comprehensive experimentation. At the present time, the essential data and structure-electronic function relationships to decipher the genetic code (generic descriptors) of metal-to-insulator transitions do not exist in a format which permits predictive synthesis. The project?s significance is that it recasts the problem into one of atomic structure, focusing on the role of different kinds of structural distortions, notably, breathing modes, Jahn-Teller distortions, and Peierls-like instabilities across a broad range of structure types and chemistries. The project will generate and collect a range of data that will permit the mapping of electronic interactions into atomic features, applying informatics-based methods to enable supervised and unsupervised learning. The project will articulate predictive rules and protocols for selecting high-performing materials, leading to an expanded palette of compounds that could impact technologies beyond electronics. The teaching and training of students at multiple levels and the discovery capabilities of the project are interwoven and aimed at broadening participation by through public outreach events, through the development of modules for undergraduate and graduate courses; and finally, by involving students in interdisciplinary team environments. The project will yield a plethora of new data on a range of oxides and new computational materials approaches. These will be aggregated into databases on public web-portals using a new portable file format designed for materials data. New methods of data visualization will allow external users to interact, query, and analyze the data for aims beyond those proposed herein. Data-driven models and informatics workflows for generating quantitative models for metal-to-insulator performance will be hosted with the aforementioned data and visualization tools on the MIST: Metals and Insulators by Structural Tuning platform. The PIs also plan to release MIST as open source and build a user community around the platform by ensuring that interested researchers are able to contribute to the MIST codebase. This will allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award. Advances in synthesis, theory, and characterization will strengthen the scientific capabilities and workforce by allowing students and academic or industrial researchers to employ the formulated structure-property relationships for educational and research purposes.