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.

该建议支持新颖的计算机科学研究，为新技术提供基础。 该建议的研究目标是设计新的可靠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。美国政府已经启动了材料基因组倡议研究，以发现，制造和部署高级材料快速和低成本，这有很大的机会来应对清洁能源，国家安全和人类福利的挑战。但是，主要的计算挑战是由于规模和复杂性而导致的全面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发现。为了解决挖掘如此全面的异质材料基因组数据的关键和具有挑战性的问题，PIS建议开发一种新颖的健壮数据挖掘，并探索整合来自多个数据源的特征的方法。 PI将在线提供开发的计算方法和工具，可供公众使用。这些方法和工具有望影响其他材料基因组和生物化学研究，并使从事新材料设计的研究人员有效地测试了性能预测假设。所提出的算法和工具有望帮助知识提取，以在具有庞大的高维和异质数据集的更广泛的科学领域的应用中提取知识。该项目将促进开发新颖的教育工具，以增强当前的几门课程。PIS建议开发一种新颖的强大数据挖掘框架，以探索以下研究任务。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，包括通过新的强大级别K矩阵完成方法插入的缺失值，基于强大的张量分解的功能提取方法以及使用可靠的主动学习模型选择的纳米颗粒选择。其次，PI将研究新的稀疏多任务多视图学习模型，以整合异质材料特征，以预测催化能力和与理论建模测量的关联。第三，为了预测新合成的纳米颗粒的催化能力，PIS将通过研究弹性嵌入，自适应损失，L1-norm图和定向图模型来设计新颖的鲁棒半监督学习模型。 提出的稀疏多视图特征学习和强大的半监督学习模型满足了大规模数据分析和集成的关键需求。如此独特的功能将在大量研究领域中实现新的计算应用。它进步，从而扩展了工程创新与计算分析之间的关系。