CDS&E/Collaborative Research: Genetic Algorithm Driven Hybrid Computational/Experimental Engineering of Defects in Designer Materials

CDS

• 批准号: 1404967
• 负责人: Alexander Balandin
• 金额: $ 16.8万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404967&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Genetic; Algorithm

The goal of this collaborative research is to build a computational framework with experimental validation to systematically engineer designer materials that provide targeted properties. Defects in materials significantly influence their properties, for instance in energy transfer. The current state-of-the-art techniques allow one to induce defects in materials controllably and subsequently predict and correlate relevant properties to the defect concentration. In this research, computational and experimental approaches will be integrated to construct designer defect-engineered materials that will provide desired properties. In this approach, first a targeted property will be ascertained, and then the defect concentration and distribution will be predicted to synthesize novel material structures that provide the predetermined property. The hybrid computational framework will motivate the general scientific community to leverage advanced computing infrastructures. The efforts will establish new research and learning communities for computational-experimental data-enabled science and engineering through promotion of diversity and undergraduate research experience, outreach to community college students and work-force development, outreach to the general public (through education about the power of simulation-based engineering with an online gaming tool and hands-on activities at science centers) and integration of research with education. This project is a comprehensive effort to rigorously integrate developments in high throughput synthesis and characterization with improving access and availability to high performance computing resources towards solving an inverse design of materials problem. A computational framework that sweeps through millions of possible structural permutations and combinations for property prediction will be developed and validated against experiments. The framework will (1) employ massively parallel molecular dynamics simulations to predict transport properties of nanomaterials, (2) synthesize defect engineered nanostructures and measure the corresponding transport properties, (3) integrate the above nanoscale computations and experiments through a genetic algorithm based hybrid optimization scheme to predict the optimal material structure for specified transport properties and formulate a hierarchy of increasingly complex duals of cost-functionals and design parameters, and finally (4) close the loop by validating the inverse design results experimentally.

这项协作研究的目的是通过对提供有针对性属性的系统工程设计师材料进行实验验证建立一个计算框架。材料的缺陷显着影响其特性，例如能量传递。当前的最新技术使人们可以控制材料中的缺陷，然后预测并将相关特性与缺陷浓度相关。在这项研究中，计算和实验方法将集成到构建设计人员缺陷工程材料，以提供所需的特性。在这种方法中，首先将确定目标特性，然后将预测缺陷浓度和分布综合提供预定特性的新型材料结构。混合计算框架将激发一般科学界利用先进的计算基础架构。这项努力将通过促进多样性和本科研究经验，向社区大学生宣讲以及向公众开发（通过对基于模拟的工程的能力，通过在线游戏中的在线游戏工具和科学中心的动力活动）以及与教育的研究集成，为基于数据支持数据的科学和工程建立了新的研究和学习社区。该项目是一项全面的努力，可以严格整合高吞吐量综合和表征的发展，并提高对高性能计算资源的访问和可用性，以解决材料问题的倒数设计。将开发和验证实验的计算框架，该计算框架将通过数百万个可能的结构排列和财产预测组合进行验证。 The framework will (1) employ massively parallel molecular dynamics simulations to predict transport properties of nanomaterials, (2) synthesize defect engineered nanostructures and measure the corresponding transport properties, (3) integrate the above nanoscale computations and experiments through a genetic algorithm based hybrid optimization scheme to predict the optimal material structure for specified transport properties and formulate a hierarchy of increasingly complex duals成本功能和设计参数，最后（4）通过实验验证逆设计结果来关闭循环。