Molecular Semulation: A New Paradigm in Materials Modeling

技术总结材料研究部、数学科学部和网络基础设施办公室为该奖项提供资金。该奖项支持计算和统计研究，以及旨在开发一种新的材料建模技术的教育，在这种技术中，晶体塑性的大规模分子动力学模拟被模拟为具有统计派生关系，该关系将输出空间缺陷密度映射到输入原子配置。一系列精心挑选的材料对高应变率变形的塑性响应的模拟被用来创建数据，并根据这些数据开发统计模型。这些模型又被用来预测不属于数据库的输入配置的最终缺陷密度。为了简洁地描述这一新概念，“模拟”一词借用自计算机科学，在计算机科学中，它被用来定义将模拟和模拟结合在一起的过程或工具。通过为每个原子分配一组值来创建能够进行统计分析的映射，这些值提供了对成键环境的唯一测量。该信息隐含地包括缺陷和晶格类型，然后被粗粒化以创建缺陷密度的空间分布。为了在统计上关联结果，粗粒数据被汇集在相同和邻近子区域的不同原子环境的计数上。将分发LAMMPS子程序形式的工具，以便研究人员能够开发自己的数据库，并将创建一个允许访问现有数据库的网络界面。非技术总结材料研究司、数学科学司和网络基础设施办公室为该奖项提供资金。该奖项支持计算和统计研究，以及旨在开发新材料建模技术的教育。数据库搜索和统计分析在广泛的应用中发挥着越来越重要的作用。例如，当前的语言翻译软件不是试图应用语法和句法规则，而是依赖于搜索包含已翻译文件的数据库。PI旨在开发一个类似的概念来模拟金属中的损伤，方法是创建大规模原子模拟生成的损伤分布数据库，然后使用这些数据库有效地预测原始模拟中未包括的新条件下的损伤。这需要开发新的统计分析方法，可以在金属变形后将缺陷分布映射回金属的初始结构。这些工具和相关的基于网络的界面将被分发给计算材料科学界，以便研究人员既可以开发自己的数据库，也可以使用现有的数据库来预测金属的损伤，而不必进行完整的模拟。

TECHNICAL SUMMARYThe Division of Materials Research, the Division of Mathematical Sciences, and the Office of Cyberinfrastructure contribute funds to this award. This award supports computational and statistical research, and education with the aim of developing a new materials modeling technique in which large-scale molecular dynamics simulations of crystal plasticity are emulated with statistically-derived relations that map output spatial defect densities to input atomic configurations. A series of carefully chosen simulations of the plastic response of materials to high strain rate deformation is used to create data from which the statistical models are developed. These models are in turn used to predict resulting defect densities for input configurations that are not part of the data base. To succinctly describe this new concept, the word "semulation" is borrowed from computer science where it has been used to define a process or tool that combines the best of simulation and emulation. Mappings that enable statistical analysis are created by assigning to each atom a set of values that provide a unique measure of bonding environment. This information, which implicitly includes defect and lattice type, is then coarse grained to create a spatial distribution of defect densities. To statistically correlate the results, the coarse grain data is pooled across counts of different atom environments in the same and nearby subregions. Tools in the form of LAMMPS subroutines will be distributed so that researchers can develop their own data bases, and a web interface will be created that will allow access to existing data bases. NON TECHNICAL SUMMARYThe Division of Materials Research, the Division of Mathematical Sciences, and the Office of Cyberinfrastructure contribute funds to this award. This award supports computational and statistical research, and education with the aim of developing a new materials modeling technique. Data base searching and statistical analysis is playing an increasingly important role in a wide range of applications. For example, rather than trying to apply grammar and syntax rules, current language translation software relies on searches of data bases that contain already translated documents. The PI aims to develop a similar concept for modeling damage in metals by creating databases of damage profiles generated from large-scale atomic simulations, and then using these databases to efficiently predict damage for new conditions that are not included in the original simulations. This requires developing new statistical analysis methods that can map defect distributions after a metal is deformed back to the metal's initial structure. These tools and associated web-based interfaces will be distributed to the computational materials science community so that researchers can both develop their own data bases, and use existing data bases to predict damage in metals without having to carry out a full simulation.