DMREF: Collaborative Research: Predictive Modeling of Polymer-Derived Ceramics: Discovering Methods for the Design and Fabrication of Complex Disordered Solids

DMREF：协作研究：聚合物衍生陶瓷的预测建模：探索复杂无序固体的设计和制造方法

• 批准号: 1729086
• 负责人: Jinwoo Hwang
• 金额: $ 29.69万
• 依托单位: Ohio State 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=1729086&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Predictive; Modeling

Non-technical Description: In the broader context of the materials-by-design grand challenge, this project will focus on developing a novel methodology for accurate design and fabrication of complex disordered solids using a combination of advanced computational and experimental techniques. Complex disordered solids are non-crystalline materials for which the fundamental building blocks are typically molecules or molecule fragments, and therefore they have great potential for tunable structure and properties for various applications of great scientific and technological importance. The key feature of our novel approach is to develop an efficient iterative loop that involves simulating the atomic structure of complex disordered solids, subsequently characterizing the resultant structures/properties, and sending the information back to fabrication conditions for further optimization. This new development is significant because it will demonstrate a computation-based design principle for systematically obtaining the growth parameters needed to make complex disordered materials with targeted properties. Ultimately, that ability can be directed to produce materials that are optimized for particular applications. It is envisioned that the results of this project will be transferrable to a wide range of complex disordered material types, growth methods, and structural/functional properties. The complete system is designated as the amorphous materials designer (AMD) program. During the construction of the AMD, students from high school up though Ph.D. graduate school will be trained by the investigators in all aspects of the research including materials simulation, fabrication, and characterization using advanced state-of-the-art methods.Technical Description: The research will focus on developing an ab initio molecular dynamics (AIMD) and hybrid reverse Monte Carlo (HRMC) simulation algorithm, augmented by ab initio based energy constraints, that couples with experimental input and feedback, using a series of thin-film amorphous preceramic polymers (a-BC:H, a-SiBCN:H, and a-SiCO:H) as suitably complex and technologically relevant case studies. The unique utility of modern solid-state nuclear magnetic resonance techniques to obtain specific bonding and connectivity information and the sensitive medium-range order information available from fluctuation electron microscopy - a specialized technique based on transmission electron microscopy - will be combined with neutron diffraction and more routine physical and electronic structure characterization methods to provide input and constraints for the simulations. The HRMC modeling efforts will be optimized via particle swarm optimization and subsequently used to train an artificial neural network (ANN) that will predictively link the parameters used to simulate a desired material with the growth parameters needed to fabricate said material. Consequently, the investigators expect to substantially advance the state of the art and surmount traditional challenges associated with (1) identifying non-global potential energy minima for materials produced under non-thermodynamic conditions and (2) aligning simulation and growth process timescales. This effort will benefit technology and society by advancing the science of design of complex disordered solids. The novelty of the effort lies in developing the algorithms and rule-sets that will tie together growth, characterization, and simulation, as well as in developing strategies for mapping (not necessarily reproducing) fabrication conditions and desired properties, and it is this that takes the effort from evolutionary to potentially revolutionary. The PIs also plan to release the AMD program as open source and build a user community around it by ensuring that interested researchers are able to contribute to the AMD 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.

非技术描述：在材料设计大挑战的更广泛背景下，该项目将重点开发一种新颖的方法，结合先进的计算和实验技术来精确设计和制造复杂的无序固体。复杂的无序固体是非晶体材料，其基本构件通常是分子或分子片段，因此它们在具有重大科学和技术重要性的各种应用中具有可调节结构和性能的巨大潜力。我们的新方法的关键特征是开发一个有效的迭代循环，其中涉及模拟复杂无序固体的原子结构，随后表征所得结构/属性，并将信息发送回制造条件以进行进一步优化。这一新进展意义重大，因为它将展示基于计算的设计原理，用于系统地获得制造具有目标特性的复杂无序材料所需的生长参数。最终，这种能力可以用于生产针对特定应用进行优化的材料。预计该项目的结果将可应用于各种复杂的无序材料类型、生长方法和结构/功能特性。完整的系统被指定为非晶材料设计师（AMD）计划。在AMD的建设过程中，学生从高中一直到博士。研究生院将接受研究人员在研究各个方面的培训，包括使用先进的最先进方法的材料模拟、制造和表征。技术描述：该研究将重点开发从头算分子动力学（AIMD）以及混合反向蒙特卡罗 (HRMC) 模拟算法，通过从头开始的能量约束进行增强，该算法与实验输入和反馈相结合，使用一系列薄膜非晶态陶瓷前体聚合物 (a-BC:H、 a-SiBCN:H 和 a-SiCO:H）作为适当复杂且技术相关的案例研究。现代固态核磁共振技术的独特用途是获得特定的键合和连接信息以及波动电子显微镜（一种基于透射电子显微镜的专门技术）提供的敏感中程有序信息，该技术将与中子衍射等相结合常规物理和电子结构表征方法，为模拟提供输入和约束。 HRMC 建模工作将通过粒子群优化进行优化，随后用于训练人工神经网络 (ANN)，该网络将预测性地将用于模拟所需材料的参数与制造所述材料所需的生长参数联系起来。因此，研究人员期望大幅提升现有技术水平并克服与以下相关的传统挑战：（1）确定非热力学条件下生产的材料的非全局势能最小值；（2）调整模拟和生长过程时间尺度。这项努力将通过推进复杂无序固体的设计科学来造福技术和社会。这项工作的新颖之处在于开发将生长、表征和模拟结合在一起的算法和规则集，以及开发映射（不一定复制）制造条件和所需属性的策略，而这正是需要的。从进化到潜在革命的努力。 PI 还计划以开源方式发布 AMD 程序，并围绕该程序建立一个用户社区，确保感兴趣的研究人员能够为 AMD 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。

项目成果

Nanoscale Structure-Property Relationship in Amorphous Hydrogenated Boron Carbide for Low- k Dielectric Applications

用于低 k 介电应用的非晶态氢化碳化硼的纳米级结构-性能关系

• DOI: 10.1017/s1431927617008091
• 发表时间: 2017
• 期刊: Microscopy and Microanalysis
• 影响因子: 2.8
• 作者: Im, Soohyun;Paquette, Michelle M.;Belhadj-Larbi, Mohammed;Rulis, Paul;Sakidja, Ridwan;Hwang, Jinwoo
• 通讯作者: Hwang, Jinwoo

Direct Determination of Medium Range Ordering in Amorphous Hydrogenated Boron Carbide for Low-k Dielectric Applications

直接测定低 k 电介质应用中非晶态氢化碳化硼的中程有序度

• DOI: 10.1017/s143192762001394x
• 发表时间: 2020
• 期刊: Microscopy and Microanalysis
• 影响因子: 2.8
• 作者: Gharacheh, Mehrdad Abbasi;Im, Soohyun;Johnson, Jared;Ortiz, Gabriel Calderon;Zhu, Menglin;Oyler, Nathan;Paquette, Michelle;Rulis, Paul;Sakidja, Ridwan;Hwang, Jinwoo
• 通讯作者: Hwang, Jinwoo