DMREF: Collaborative Research: Complex Nanofeatures in Crystals: Theory and Experiment Meet in the Cloud

DMREF：协作研究：晶体中的复杂纳米特征：理论与实验在云端相遇

• 批准号: 1922234
• 负责人: Simon Billinge
• 金额: $ 115万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922234&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Complex; Nanofeatures

Non-technical Description: Everything, from phones to people, are made of materials. As mankind seeks technological solutions to its biggest problems, we constantly seek materials that will do their tasks with higher performance. We want batteries with higher energy density, screens that are touch sensitive, windows that are smart, and so on. Inevitably, the search for better materials leads to greater complexity in the materials themselves, including nanostructuring them: engineering them on a tiny scale of one billionth of a meter. Recent investigations have shone light on a previously overlooked class of materials: bulk crystals that naturally have nanoscale broken symmetry structures patterned over the average crystal structure. These were overlooked because they are hard to detect experimentally, something which recent developments in experimental techniques is overcoming. Since they weren't known about, there was no theoretical effort to find and understand them. However, recently theoretical support for their existence has come through the discovery that such nanostructured symmetry broken structures may, in some cases, be energetically more stable than the undistorted parent structure. Such materials are being referred to as polymorphous network materials (PNMs). The goal of this Designing Materials to Revolutionize and Engineer our Future (DMREF) project is to understand the origin of this mysterious materials complexity and with the greater understanding, to discover new PNMs. (Often nature prefers simpler, high symmetry solutions to its problems. Why is it not the case in these PNM materials?) The classes of material that are known PNMs are transition metal oxides, halides and chalcogenides. These are among the most interesting materials scientifically (exhibiting exotic but poorly understood effects such as the ability to turn from a metal to an insulator as a function of temperature or field, high temperature superconductivity and colossal responses to applied fields) and with many potential technological applications. This project will combine the theory and the experimental developments with cloud-based computational infrastructure that will allow a broader range of researchers to search for novel PNMs, and to understand the existing ones better. A key aspect of this project will involve the training of the next generation of scientists and engineers in the use of the Pair Distribution Function methodology in the cloud platform. The PIs will educate US and African graduate students in the interdisciplinary research philosophy integral to the Materials Genome Initiative. Technical Description: The project will combine computation to predict, synthesis to make, and x-ray and neutron local structure characterization to validate the predictions, an approach that embodies the Materials Genomics philosophy and applies it to PNMs. Quantum mechanical density functional theory (DFT) calculations will be applied to supercells of transition metal oxides and chalcogenides that are sufficiently large to support the PNM effect, to see if nanostructured distortions can lower the total energy. These will be applied to classes of known materials, such as hybrid organic-inorganic halides, to search for and characterize the nature of the PNM distortions. The most promising materials will be synthesized and characterized using PDF, a diffraction method sensitive to the local distortions. A computational infrastructure will be built that will save results, both theoretical and experimental, to databases for later mining. The infrastructure will be made available to the community to carry out their own computational 'experiments'.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.

非技术描述：从手机到人，一切都是由材料制成的。当人类寻求技术解决方案来解决其最大的问题时，我们不断寻求能够以更高性能完成任务的材料。 我们需要能量密度更高的电池、触摸感应的屏幕、智能的窗户等等。对更好材料的探索不可避免地导致了材料本身的更大复杂性，包括纳米结构：在十亿分之一米的微小尺度上对其进行工程设计。最近的研究揭示了以前被忽视的一类材料：在平均晶体结构上自然具有纳米级对称性破缺结构的大块晶体。这些被忽略了，因为它们很难通过实验检测出来，而最近的实验技术的发展正在克服这一点。由于它们不为人所知，因此没有理论上的努力来寻找和理解它们。然而，最近的理论支持他们的存在已经通过发现，这种纳米结构的对称性破缺结构，在某些情况下，可能是积极的更稳定的比未扭曲的母结构。这种材料被称为多晶型网络材料（PNM）。这个设计材料以革命和工程我们的未来（DMREF）项目的目标是了解这种神秘材料复杂性的起源，并通过更深入的了解，发现新的PNM。（大自然往往倾向于用更简单、更对称的方法来解决问题。为什么在这些PNM材料中不是这样？）已知PNM的材料种类是过渡金属氧化物、卤化物和硫属化物。它们是科学上最有趣的材料之一（表现出奇异但知之甚少的效应，例如从金属变成绝缘体的能力，作为温度或场的函数，高温超导性和对外加场的巨大响应），并具有许多潜在的技术应用。该项目将联合收割机的理论和实验的发展与基于云的计算基础设施，将允许更广泛的研究人员寻找新的PNM，并更好地了解现有的。该项目的一个关键方面将涉及下一代科学家和工程师在云平台中使用配对分布函数方法的培训。该PI将教育美国和非洲的研究生在跨学科的研究理念不可或缺的材料基因组计划。技术说明：该项目将结合联合收割机计算来预测，合成，X射线和中子局部结构表征来验证预测，这是一种体现材料基因组学哲学并将其应用于PNM的方法。 量子力学密度泛函理论（DFT）计算将应用于过渡金属氧化物和硫属化物的超级电池，这些超级电池足够大以支持PNM效应，以观察纳米结构的扭曲是否可以降低总能量。这些将被应用到类已知的材料，如混合有机-无机卤化物，搜索和表征的PNM失真的性质。最有前途的材料将使用PDF进行合成和表征，PDF是一种对局部失真敏感的衍射方法。将建立一个计算基础设施，将理论和实验结果保存到数据库中，供以后挖掘。该基础设施将提供给社区进行自己的计算“实验”。该奖项反映了NSF的法定使命，并已被认为是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的支持。

项目成果

Robustness test of the spacegroupMining model for determining space groups from atomic pair distribution function data

从原子对分布函数数据确定空间群的空间群挖掘模型的鲁棒性测试

• DOI: 10.1107/s1600576722002990
• 发表时间: 2022
• 期刊: Journal of Applied Crystallography
• 影响因子: 6.1
• 作者: Lan, Ling;Liu, Chia-Hao;Du, Qiang;Billinge, Simon J.
• 通讯作者: Billinge, Simon J.

Autonomous experimentation systems for materials development: A community perspective

• DOI: 10.1016/j.matt.2021.06.036
• 发表时间: 2021-07
• 作者: E. Stach;Brian L. DeCost;A. Kusne;J. Hattrick-Simpers;Keith A. Brown;Kristofer G. Reyes;Joshua Schrier;S. Billinge;T. Buonassisi;Ian T Foster;Carla P. Gomes;J. Gregoire;Apurva Mehta;Joseph H. Montoya;E. Olivetti;Chiwoo Park;E. Rotenberg;S. Saikin;S. Smullin;V. Stanev;B. Maruyama

Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models

使用条件生成模型根据 X 射线散射数据表征单金属纳米颗粒的原子结构

• DOI: 10.26434/chemrxiv.12662222.v1
• 发表时间: 2020
• 期刊: ChemRxiv
• 作者: Anker, Andy S.;Kjaer, Emil T.;Dam, Erik B.;Billinge, Simon J.;Jensen, Kirsten M.;Selvan, Raghavendra
• 通讯作者: Selvan, Raghavendra

In Situ Studies of the Formation of Tungsten and Niobium Oxide Nanoparticles: Towards Automated Analysis of Reaction Pathways from PDF Analysis using the Pearson Correlation Coefficient

钨和氧化铌纳米粒子形成的原位研究：使用皮尔逊相关系数从 PDF 分析中自动分析反应路径

• DOI: 10.1002/cmtd.202200034
• 发表时间: 2022
• 期刊: Chemistry–Methods
• 作者: Kjær, Emil T. S.;Aalling‐Frederiksen, Olivia;Yang, Long;Thomas, Nancy K.;Juelsholt, Mikkel;Billinge, Simon J. L.;Jensen, Kirsten M. Ø.
• 通讯作者: Jensen, Kirsten M. Ø.

Understanding electronic peculiarities in tetragonal FeSe as local structural symmetry breaking

• DOI: 10.1103/physrevb.102.235121
• 发表时间: 2020-12
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Zhi Wang;Xingang Zhao;R. Koch;S. Billinge;A. Zunger