The Ab-Initio Prediction of Crystal Structure: Combining Data Mining Ideas with Quantum Mechanics

晶体结构的从头算预测：数据挖掘思想与量子力学的结合

• 批准号: 0606276
• 负责人: Gerbrand Ceder
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0606276&HistoricalAwards=false
• 关键词: Ab; Initio; Prediction; Crystal; Structure

TECHNICAL SUMMARY:This award supports computational and theoretical research that aims to develop a rigorous formalism to capture "knowledge" from past experimental and computed data, and use it to rapidly guide accurate quantum mechanical energy or free energy methods towards the most stable structure in binary and ternary metallic alloys and oxides. In a departure from traditional computational approaches, the PI will merge ideas from data-mining to extract knowledge from the large body of existing crystal structure information with the predictive power of quantum mechanical calculations. The PI will pursue a probabilistic approach. The probability of particular structure to appear in a new alloy is expanded in terms of correlations between structures at different compositions and between structures and elements. To capture structure correlations present in nature, the PI will data mine some of the largest databases available for metallic alloys and oxide compounds and construct a maximum entropy representation of it. This will enable predictions for many alloys for which currently little or no characterization is present. The resulting structure prediction tool and all in-house generated data will be made available to the research community as a web-based structure predictor so that these new methods can be most efficiently disseminated.The new developments gained from this research, and the ab-initio database that will be created, will be integrated with the freely available (on the web) electronic course on Computational Materials Science the PI teaches. This contributes to the cyberinfrastructure of the materials research community.NON-TECHNICAL SUMMARY:This award supports computational and theoretical research that aims to develop a rigorous formalism to capture "knowledge" from past experimental and computed data, and use it to rapidly guide accurate computations that aim to predict how atoms will organize themselves in materials. The PI will focus on classes of alloys and oxide materials. Crystal structure plays a fundamental and widely applicable role in materials science. Many relevant physical properties of inorganic materials are directly tied to, and sometimes prohibited by, the underlying symmetry of the way atoms arrange themselves in a crystal. In computational materials science where one tries to predict properties of materials before they are synthesized, the prediction of structure is a key but a missing cornerstone of materials design by computer. This work contributes to efforts to develop computational methods that can predict the way atoms will arrange themselves in a crystal.This work contributes to the cyberinfrastructure of the materials research community. It involves the novel application of data mining to materials computations and the use of the resulting integration to solve complex materials problems. The successful completion of this research project will lead to an approach that can determine the stable arrangement of atoms in a material with a high confidence level, and to the creation of a database available to the public that contains the results of computations for a large number of crystal structures and alloys that can be queried by theorists, computational materials researchers, experimentalists, students, and materials educators.

技术摘要：该奖项支持计算和理论研究，旨在开发一种严格的形式主义，以从过去的实验和计算数据中捕获“知识”，并使用它来快速指导准确的量子机械能或自由能方法，以实现二元和三元金属合金和氧化物中最稳定的结构。在与传统的计算方法背道而驰的情况下，PI将从数据挖掘中合并思想，从现有的晶体结构信息中提取知识，并与量子机械计算的预测能力相结合。 PI将采用概率方法。出现在新合金中的特定结构的可能性是根据不同组成和结构和元素之间结构之间的相关性扩展的。为了捕获自然界中存在的结构相关性，PI将数据挖掘一些用于金属合金和氧化物化合物的最大数据库，并构建其最大熵表示。这将对许多当前几乎没有表征的合金进行预测。最终的结构预测工具和所有内部生成的数据将作为基于网络的结构预测器提供给研究社区，以便可以最有效地传播这些新方法。从这项研究中获得的新开发项目，以及将创建的AB-Initio数据库，将与免费的计算材料科学的电子课程集成到PI Theches Theches Theches Theches Theches Theches theches pi Thectes。这有助于材料研究界的网络基础结构。非技术摘要：该奖项支持计算和理论研究，旨在开发一种严格的形式主义，以从过去的实验和计算数据中捕获“知识”，并利用它来快速指导准确的计算，以预测原子在材料中如何组织自身。 PI将专注于合金和氧化物材料的类别。晶体结构在材料科学中起基本且广泛适用的作用。无机材料的许多相关物理特性直接与原子方式的潜在对称性直接绑定，有时被限制在晶体中。在计算材料科学中，人们试图在材料合成之前预测材料的特性，结构的预测是计算机设计的材料设计的关键，但缺失了材料设计的基石。 这项工作有助于开发可以预测原子方式将自己安排成晶体的计算方法的努力。这项工作有助于材料研究界的网络基础设施。它涉及将数据挖掘在材料计算中的新应用以及所得整合来解决复杂材料问题的使用。该研究项目的成功完成将导致一种方法，该方法可以确定具有较高置信度的材料中原子的稳定布置，并为公众提供的数据库创建包含用于大量晶体结构和合金的计算结果，这些晶体结构和合金可以由理论家，计算材料研究人员，实验室研究员，实验室研究员，学生和材料教育师进行查询。