CARM: Computational Infrastructure for Intelligent Material Design

该项目的目标是开发和应用可扩展的数值方法和软件基础设施，用于材料性质的第一性原理预测。这些材料通常具有复杂的结构和组成，大量的原子（n100）和很少或没有对称性。对于许多重要的例子，使用目前的方法是不可能进行可靠的理论预测的。这里正在测试一种新方法；初步结果表明，它有望消除将第一性原理计算广泛应用于实际材料的主要障碍，从而大大扩展可处理的问题。作为该项目的一部分，这种新方法将应用于一个重要的问题，即碳基材料的氧化。该项目建立在一种新的计算方法来解决基于并行，实空间，自适应多网格特征值求解器的LAD方程。与傅立叶方法不同，该自适应方法对解进行局部细化，以捕获问题中的短长度尺度；例如，碳氧体系中近氧区的短长度。研究人员的初步计算目前仅限于与实际材料的薛定谔特征值方程结构相似的模型问题。然而，与现有算法相比，新的计算方法在收敛性和效率方面都有很大的提高。该程序的目的是增强所提出的求解器，使自适应方法应用于实际材料。

The goal of this project is to develop and apply scalable numerical methods and a software infrastructure for the first principles prediction of the properties of materials. These materials typically have complex structures and compositions, a large number of atoms (n100) and little or no symmetry. For many important examples reliable theoretical predictions are not possible using present day approaches. Here a new method is being tested; preliminary results show that it promises to remove a major roadblock to the wider application of first principle calculations to real materials, thereby greatly expanding the problems that can be treated. As part of the project this new method will be applied to an important problem, the oxidation of carbon based materials. The project builds on a novel computational approach to solving the LAD equations based on a parallel, real-space, adaptive multigrid eigenvalue solver. Unlike Fourier methods, this adaptive method locally refines the solution to capture the short length scales in the problem; for example, the short length in near oxygen region in the carbon oxygen system. The investigators' preliminary calculations are currently limited to model problems with structures similar to the real materials Schrodinger eigenvalue equation. However, the new computational method shows promising improvements in convergence and efficiency over present algorithms. The purpose of this program is to enhance the proposed solver to enable application of an adaptive method to real materials.