ITR: Data Mining of Quantum Mechanical Calculations for Predicting Materials Structure

该奖项是根据ITR招标NSF-02-168提交的“小型”类别提案而颁发的。它支持使用数据挖掘技术对从头计算方法获得的数据进行计算研究和教育，以预测新材料的晶体结构。从头算方法正成为物理学家、化学家和材料科学家的普遍工具。这些方法使科学家能够“在计算机上”评估和预筛选新材料，而不是通过耗时的实验。目前从头计算的一个局限性是，这种方法不能积累经验或知识（除了科学家的技能增加）。例如，当计算合金的稳定性如何作为温度和成分的函数而变化时，每个新系统都独立于先前在其他系统上获得的结果进行处理。这项工作的目标是探索一种完全不同的方法，该方法使用数据挖掘方法，利用从其他系统上收集的结果中获得的知识，为新的从头算研究提供信息。本研究的目的是展示量化的知识提取大量的从头计算，并使用这些知识的晶体结构的预测。从头计算将使用精确和完善的密度泛函理论技术进行。知识提取技术将从新兴的数据挖掘领域借鉴。这些技术在工业、电子商务、社会、化学和生物科学中的应用越来越多。研究将首先集中在线性技术，例如，多元回归方法，如主成分分析和偏最小二乘法，然后包括使用神经网络和聚类算法的非线性方法，以及利用更成熟的技术，如聚类扩展。该网络将便利这项工作，使人们能够从整个从头开始的社区收集数据，并提供一个中央公共资源，可以测试和储存数据和数据挖掘工具。从这项研究中获得的新进展，以及将创建的从头算数据库，将与PI在计算材料建模方面的教学活动相结合。在教室和计算实验室中使用将特别有助于学生学习材料的结构和能量之间的关系。PI的方法可能对材料研究和设计产生重大影响。该研究项目的成功完成可能会导致确定材料稳定结构的可靠方法，并创建大量晶体结构和合金的从头计算能量的公共数据库，可供从头计算从业者，实验研究人员，学生和材料教育工作者查询。该奖项是根据ITR招标NSF-02-168提交的“小型”类别提案而颁发的。它支持使用数据挖掘技术对从头计算方法获得的数据进行计算研究和教育，以预测新材料的晶体结构。 预测新合金的晶体结构只知道组成原子的身份是材料科学中长期存在的基本挑战，也是有效的第一原理材料设计的主要障碍。本研究的目的是展示从大量的基于密度泛函理论的计算中提取可量化的知识，并将这些知识用于晶体结构的预测。已经应用于工业、电子商务、社会、化学和生物科学的知识提取技术将从数据挖掘领域借鉴。该网络将便利这项工作，使人们能够从社区收集数据，并提供一个中央公共资源，供测试和储存数据和数据挖掘工具。从这项研究中获得的新进展，以及将创建的数据库，将与PI在计算材料建模方面的教学活动相结合。在教室和计算实验室中使用将特别有助于学生学习材料的结构和能量之间的关系。PI的方法可能对材料研究和设计产生重大影响。该项目可能导致确定材料稳定结构的可靠方法，并创建大量晶体结构和合金的计算能量的公共数据库，可供理论材料科学家，实验研究人员，学生和教育工作者查询。

This award was made on a 'small' category proposal submitted in response to the ITR solicitation, NSF-02-168. It supports computational research and education on using data-mining techniques on data obtained using ab-initio methods to predict crystal structures of new materials. Ab-initio methods are becoming ubiquitous tools for physicists, chemists, and materials scientists. These methods allow scientists to evaluate and pre-screen new materials "in silico", rather than through time-consuming experimentation. A current limitation of ab-initio computation is that the method does not accumulate experience or knowledge (except for an increase in the skills of the scientist). For example, when calculating how the stability of alloys changes as a function of temperature and composition, each new system is treated independently of results one may have obtained previously on other systems. The goal of this work is explore a radically different approach, which uses data mining methods to inform new ab-initio investigations with knowledge obtained from results already collected on other systems. The objective of this research is to demonstrate quantifiable knowledge extraction from a large number of ab-initio calculations, and to use this knowledge in the prediction of crystal structure. The ab-initio calculations will be carried out using accurate and well-established techniques of density functional theory. Knowledge extraction techniques will be borrowed from the burgeoning world of data mining. These techniques have found growing applications in industry, e-commerce, and the social, chemical, and biological sciences. The research will initially focus around linear techniques, for example, multivariate regression methods like Principal Component Analysis and Partial Least Squares, and then include non-linear approaches using neural networks and clustering algorithms, as well as make use of more established techniques, such the cluster expansion. The web will facilitate this work by making it possible to gather data from the entire ab-initio community, and by providing a central public resource where data and data mining tools can be tested and stored. New developments gained from this research, and the ab-initio database that will be created, will be integrated with the teaching activities of the PI in computational materials modeling. Use in the classroom and computational laboratory will particularly assist students in learning the relationships between structure and energetics of materials. The PI's approach may have substantial impact on materials research and design. The successful completion of this research project may lead to a reliable method for determining the stable structure of a material, and to the creation of a public database of ab-initio calculated energies for a large number of crystal structures and alloys that can be queried by ab-initio practitioners, experimentalist researchers, students, and materials educators.%%%This award was made on a 'small' category proposal submitted in response to the ITR solicitation, NSF-02-168. It supports computational research and education on using data-mining techniques on data obtained using ab-initio methods to predict crystal structures of new materials. Predicting crystal structures for new alloys knowing only the identity of the constituent atoms is a long standing and fundamental challenge in materials science, and a major impediment to effective first-principles materials design. The objective of this research is to demonstrate quantifiable knowledge extraction from a large number of density-functional-theory based computations, and to use this knowledge in the prediction of crystal structure. Knowledge extraction techniques that have been applied in industry, e-commerce, and the social, chemical, and biological sciences, will be borrowed from the world of data mining. The web will facilitate this work by making it possible to gather data from the community, and by providing a central public resource where data and data mining tools can be tested and stored. New developments gained from this research, and the database that will be created, will be integrated with the teaching activities of the PI in computational materials modeling. Use in the classroom and computational laboratory will particularly assist students in learning the relationships between structure and energetics of materials. The PI's approach may have substantial impact on materials research and design. This project may lead to a reliable method for determining the stable structure of a material, and to the creation of a public database of calculated energies for a large number of crystal structures and alloys that can be queried by theoretical materials scientists, experimentalist researchers, students, and educators.***