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

这项建议支持新的计算机科学研究，为新技术提供基础。该方案的研究目标是设计新的健壮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。材料基因组计划研究是由美国政府发起的，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、国家安全和人类福利方面的挑战提供了巨大机遇。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，促进新材料的发现。为了解决挖掘这种复杂的异质材料基因组数据的关键和具有挑战性的问题，PI提出了一种新的健壮的数据挖掘方法，并探索了集成多个数据源的特征的方法。公共信息系统将把开发的计算方法和工具放在网上，供公众使用。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，并使从事新材料设计的研究人员能够有效地测试性能预测假说。所提出的算法和工具有望帮助在具有海量高维和异质数据集的更广泛的科学领域中的应用程序进行知识提取。这个项目将促进新的教育工具的开发，以增强目前的几门课程。PIS建议开发一个新的健壮的数据挖掘框架，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，包括通过新的稳健秩k矩阵补全方法进行缺失值推算，基于稳健张量分解的特征提取方法，以及使用稳健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，以集成异质材料的表征，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的稳健的半监督学习模型。所提出的稀疏多视点特征学习和稳健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。这种独特的能力将使新的计算应用在大量研究领域成为可能。它促进并扩展了工程创新和计算分析之间的关系。

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.