III: Small: Collaborative Research: Robust Materials Genome Data Mining Framework for Prediction and Guidance of Nanoparticle Synthesis

III：小型：协作研究：用于预测和指导纳米颗粒合成的稳健材料基因组数据挖掘框架

• 批准号: 1423056
• 负责人: Junzhou Huang
• 金额: $ 25万
• 依托单位: University of Texas at Arlington
• 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: 2014-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423056&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Robust

This proposal supports novel computer science research to provide foundations for new technologies. The research objective of this proposal is to design new robust data mining and machine learning algorithms for solving the computational challenges in complex materials genome data mining. The Materials Genome Initiative research has been launched by U.S. government to discover, manufacture, and deploy advanced materials fast and low-cost, which holds great opportunities to address the challenges in clean energy, national security, and human welfare. However, the major computational challenges are the bottlenecks for comprehensive materials genome data analysis due to unprecedented scale and complexity. There is a critical need for new data mining and machine learning strategies to bridge the gap and facilitate the new materials discovery. To solve the key and challenging problems in mining such comprehensive heterogeneous materials genome data, the PIs propose to develop a novel robust data mining and explore ways to integrate features from multiple data sources. The PIs will make the developed computational methods and tools online, available to the public. These methods and tools are expected to impact other material genome and biochemistry research and enable investigators working on new material design to effectively test performance prediction hypothesis. The proposed algorithms and tools are expected to help knowledge extraction for applications in broader scientific domains with massive high-dimensional and heterogonous data sets. This project will facilitate the development of novel educational tools to enhance several current courses.The PIs propose to develop a novel robust data mining framework targeting to explore the following research tasks. First, the PIs will develop new computational tools to automate the material genome data processing, including missing values imputation by a new robust rank-k matrix completion method, robust tensor factorization based feature extraction approach, and informative nanoparticles selection using robust active learning model. Second, the PIs will investigate the new sparse multi-task multi-view learning model to integrate heterogeneous material characterizations for predicting the catalytic capabilities and associations to theoretical modeling measurements. Third, to predict the catalytic capabilities of the new synthesized nanoparticles, the PIs will design novel robust semi-supervised learning models by investigating elastic embedding, adaptive loss, L1-norm graph, and directed graph models. The proposed sparse multi-view feature learning and robust semi-supervised learning models meet the critical needs of large-scale data analysis and integration. Such unique capabilities will enable new computational applications in a large number of research areas. It advances and thus extends the relationship between engineering innovation and computational analysis.

该提案支持新颖的计算机科学研究，为新技术提供基础。 该提案的研究目标是设计新的稳健的数据挖掘和机器学习算法，以解决复杂材料基因组数据挖掘中的计算挑战。美国政府发起的材料基因组计划研究旨在快速、低成本地发现、制造和部署先进材料，这为应对清洁能源、国家安全和人类福祉方面的挑战提供了巨大的机遇。然而，由于前所未有的规模和复杂性，主要的计算挑战是综合材料基因组数据分析的瓶颈。迫切需要新的数据挖掘和机器学习策略来弥合差距并促进新材料的发现。为了解决挖掘此类综合异质材料基因组数据的关键和挑战性问题，PI建议开发一种新颖的鲁棒数据挖掘方法，并探索集成多个数据源特征的方法。 PI 将在线向公众提供开发的计算方法和工具。这些方法和工具预计将影响其他材料基因组和生物化学研究，并使研究新材料设计的研究人员能够有效地测试性能预测假设。所提出的算法和工具预计将有助于利用大量高维和异质数据集在更广泛的科学领域中应用知识提取。该项目将促进新型教育工具的开发，以增强当前的几门课程。PI 建议开发一种新颖的稳健数据挖掘框架，旨在探索以下研究任务。首先，PI将开发新的计算工具来自动化材料基因组数据处理，包括通过新的稳健的rank-k矩阵补全方法进行缺失值插补，基于稳健的张量分解的特征提取方法，以及使用稳健的主动学习模型选择信息丰富的纳米粒子。其次，PI 将研究新的稀疏多任务多视图学习模型，以集成异质材料表征，以预测催化能力以及与理论建模测量的关联。第三，为了预测新合成纳米粒子的催化能力，PI 将通过研究弹性嵌入、自适应损失、L1 范数图和有向图模型来设计新颖的鲁棒半监督学习模型。 所提出的稀疏多视图特征学习和鲁棒半监督学习模型满足大规模数据分析和集成的关键需求。这种独特的功能将使许多研究领域的新计算应用成为可能。它推进并扩展了工程创新和计算分析之间的关系。