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

DMREF：合作研究：金属-绝缘体转变的结构基因组

基本信息

批准号：
1729303
负责人：
James Rondinelli
金额：
$ 40万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1729303&HistoricalAwards=false
关键词：
DMREF Collaborative Research Structure Genome

项目摘要

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.

非技术描述：全世界的计算能力已经有了巨大的提高，但半导体行业当前的前进道路却充满了障碍。下一代电子设备的不同策略是基于以多种电子状态存在的材料。为此需要新一代电子材料，以及在多种电子状态之间切换的手段。该项目的重点基础科学集中于某些材料在外部触发器通过所谓的金属到绝缘体转变进行切换时电性能的突然变化。在过渡的一侧，该材料的行为类似于铜金属，而在另一侧，它的行为类似于绝缘木材。该项目的目标是设计和发现表现出这种金属到绝缘体转变的材料，这些材料能够在室温下运行，并且在感兴趣的关键属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。该项目将提供带有一套工具的研究工作流程，以对发现关键材料的新概念进行评估和实验验证。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范围。学生的教学和培训与项目的发现能力相互交织，旨在通过研究人员及其小组成员参与公共宣传活动来扩大参与范围。本科生和研究生课程模块的开发以及学生参与跨学科团队环境是项目计划的内在内容。该项目将产生大量关于一系列氧化物和新计算材料方法的新数据和挖掘数据。这些将被汇总到公共门户网站上的开放访问数据库中。技术描述：该项目将通过在晶胞级晶体结构和原子级之间建立联系的方法，寻求发现显示金属到绝缘体转变的材料的原子级遗传密码。宏观电子响应，受益于理论、数据和综合实验的结合。目前，破译金属-绝缘体转变的遗传密码（通用描述符）的基本数据和结构-电子功能关系并不以允许预测合成的格式存在。该项目的意义在于它将问题重新转化为原子结构问题，重点关注不同类型的结构畸变的作用，特别是呼吸模式、Jahn-Teller 畸变和跨多种结构类型的 Peierls 样不稳定性和化学。该项目将生成并收集一系列数据，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，从而扩大化合物范围，从而影响电子以外的技术。多层次学生的教学和培训以及项目的发现能力相互交织，旨在通过公共宣传活动、开发本科生和研究生课程模块来扩大参与；最后，让学生参与跨学科的团队环境。该项目将产生大量有关一系列氧化物和新计算材料方法的新数据。这些将使用专为材料数据设计的新便携式文件格式聚合到公共门户网站上的数据库中。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，以达到本文提出的目标之外的目的。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通过 MIST：金属和绝缘体结构调整平台上的上述数据和可视化工具进行托管。 PI 还计划将 MIST 作为开源版本发布，并围绕该平台建立一个用户社区，确保感兴趣的研究人员能够为 MIST 代码库做出贡献。这将使该项目获得更广泛的发展。高级网络基础设施办公室的软件集群对这一方面特别感兴趣，该办公室为该奖项提供了共同资助。合成、理论和表征方面的进步将允许学生和学术或工业研究人员将已制定的结构-性能关系用于教育和研究目的，从而增强科学能力和劳动力。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Featureless adaptive optimization accelerates functional electronic materials design

无特征自适应优化加速功能电子材料设计

DOI：
10.1063/5.0018811
发表时间：
2020-12
期刊：
Applied Physics Reviews
影响因子：
15
作者：
Wang, Yiqun;Iyer, Akshay;Chen, Wei;Rondinelli, James M.
通讯作者：
Rondinelli, James M.

钌 A2Ru2O7 烧绿石中不相关的 Bi 偏心和绝缘体到金属的转变

DOI：
10.1103/physrevmaterials.3.095003
发表时间：
2019-09
期刊：
Physical Review Materials
影响因子：
3.4
作者：
Laurita, Geneva;Puggioni, Danilo;Hickox;Rondinelli, James M.;Gaultois, Michael W.;Page, Katharine;Lamontagne, Leo K.;Seshadri, Ram
通讯作者：
Seshadri, Ram

Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds

用于识别热驱动金属-绝缘体过渡化合物的数据库、特征和机器学习模型

DOI：
10.1021/acs.chemmater.1c00905
发表时间：
2020-10-26
期刊：
Chemistry of Materials
影响因子：
8.6
作者：
A. Georgescu;Peiwen Ren;Aubrey Tol;Shengtong Zhang;Kyle D. Miller;D. Apley;E. Olivetti;Nicholas Wagner;J. Rondinelli
通讯作者：
J. Rondinelli

Electronic structure of negative charge transfer CaFeO3 across the metal-insulator transition

金属-绝缘体转变负电荷转移 CaFeO3 的电子结构

DOI：
10.1103/physrevmaterials.2.015002
发表时间：
2018-01
期刊：
Physical Review Materials
影响因子：
3.4
作者：
Rogge, Paul C.;Chandrasena, Ravini U.;Cammarata, Antonio;Green, Robert J.;Shafer, Padraic;Lefler, Benjamin M.;Huon, Amanda;Arab, Arian;Arenholz, Elke;Lee, Ho Nyung;et al
通讯作者：
et al

Symbolic regression in materials science

材料科学中的符号回归