High Performance Algorithms for Electronic Materials

9873664Chelikowsky和saad这是一项由材料研究部、高级计算基础设施与研究部和数学科学部联合资助的跨学科研究基金。该研究涉及在大规模并行计算机上开发和实现算法，以及使用电子结构理论计算真实材料的性质。感兴趣的材料包括非晶材料、原子团簇、液体、玻璃、扩展缺陷、重构点缺陷、非理想界面等。高性能和新型结构计算机的最新发展为应用复杂的电子结构技术来研究这些材料创造了新的机会。在不久的将来，速度可能会再有两个或两个以上的数量级的提高，这将使我们超越目前材料科学的计算极限，并进入一个令人兴奋的时代，即对真实材料的理解和发现。这些收获不仅仅是硬件的进步，而可能更多地依赖于算法的创新。在多学科研究中开发新的算法需要合作成员对不同学科的理解，在这种情况下，计算机科学家和物理科学家之间的密切合作。我们将使用真实空间算法（基于从头算伪势）来预测量子点和纳米结构物质的介电特性。我们将应用各种方法来解决这个问题，包括由密度泛函理论确定的准粒子能量。我们将考虑几千个原子大小的量子点。这将使我们能够根据光学和介电响应函数来研究从“原子”到“晶体”的转变。此外，我们将研究量子点的结构性质和外在性质，如光致发光。我们打算继续我们正在进行的其他领域的活动，如固体缺陷，半导体液体的结构和电子特性，以及晶体生长。我们将继续研究计算特征向量和特征值的新的有效算法，重点是计算大量特征向量的方法。这些算法包括，但不限于，通过多项式迭代和预处理技术的频谱切片。此外，我们将研究避免计算特征值和特征向量的新算法。其中一种方法将基于使用Lanczos程序。这是一项跨学科研究基金，由材料研究部、高级计算基础设施与研究部和数学科学部联合资助。该研究涉及在大规模并行计算机上开发和实现算法，以及使用电子结构理论计算真实材料的性质。感兴趣的材料包括非晶材料、原子团簇、液体、玻璃、扩展缺陷、重构点缺陷、非理想界面等。高性能和新型结构计算机的最新发展为应用复杂的电子结构技术来研究这些材料创造了新的机会。在不久的将来，速度可能会再有两个或两个以上的数量级的提高，这将使我们超越目前材料科学的计算极限，并进入一个令人兴奋的时代，即对真实材料的理解和发现。这些收获不仅仅是硬件的进步，而可能更多地依赖于算法的创新。在多学科研究中开发新的算法需要合作成员对不同学科的理解，在这种情况下，计算机科学家和物理科学家之间的密切合作

9873664Chelikowsky and SaadThis is an interdisciplinary research grant funded jointly by the Divisions of Materials Research, of Advanced Computational Infrastructure and Research, and of Mathematical Sciences. The research involves algorithm development and implementation on massively parallel computers, and calculations of the properties of real materials using electronic structure theory. Materials of interest include amorphous materials, atomic clusters, liquids, glasses, extended defects, reconstructed point defects, non-ideal interfaces, etc. The recent development of high performance and novel architecture computers is creating new opportunities for the application of sophisticated electronic structure techniques to study these materials. Another two or more orders of magnitude improvement in speed, which is likely in the near future, will enable us to step beyond the current computational limits in materials science and reach an exciting era of understanding and discovery in real materials. These gains are not achieved by progress in hardware alone, but may depend more on innovations in algorithms. Developing new algorithms in multidisciplinary research requires an understanding of the different disciplines involved by the collaborating members, in this case a close collaboration between computer scientists and physical scientists.We will use real space algorithms (based on ab initio pseudopotentials) to predict the dielectric properties of quantum dots and nanostructured matter. We will apply a variety of methodologies to this problem including quasiparticle energies determined from density functional theory. We will consider quantum dots up to several thousand atoms in size. This will allow us to examine the transition from "atomic" to "crystalline" in terms of optical and dielectric response functions. Also, we will examine the structural properties of quantum dots, and extrinsic properties such as photoluminescence. We intend to continue our ongoing activities in other areas such as defects in solids, the structure and electronic properties of semiconductor liquids, and crystal growth.We will continue to examine new and efficient algorithms for computing eigenvectors and eigenvalues, with an emphasis on methods for computing a large number of eigenvectors. These algorithms include, but are not limited to, spectrum slicing via polynomial iterations and preconditioning techniques. In addition, we will examine new algorithms which avoid the computation of eigenvalues and eigenvectors altogether. One of these methods will be based on utilizing the Lanczos procedure.%%% This is an interdisciplinary research grant funded jointly by the Divisions of Materials Research, of Advanced Computational Infrastructure and Research, and of Mathematical Sciences. The research involves algorithm development and implementation on massively parallel computers, and calculations of the properties of real materials using electronic structure theory. Materials of interest include amorphous materials, atomic clusters, liquids, glasses, extended defects, reconstructed point defects, non-ideal interfaces, etc. The recent development of high performance and novel architecture computers is creating new opportunities for the application of sophisticated electronic structure techniques to study these materials. Another two or more orders of magnitude improvement in speed, which is likely in the near future, will enable us to step beyond the current computational limits in materials science and reach an exciting era of understanding and discovery in real materials. These gains are not achieved by progress in hardware alone, but may depend more on innovations in algorithms. Developing new algorithms in multidisciplinary research requires an understanding of the different disciplines involved by the collaborating members, in this case a close collaboration between computer scientists and physical scientists.***