Collaborative Research: Frameworks: Scalable Modular Software and Methods for High-Accuracy Materials and Condensed Phase Chemistry Simulation

电子在材料中的排列方式导致了各种不同的行为。我们可以观察这些行为，并将其用于各种技术。然而，对这些行为的预测是一个严峻的挑战。这使得成功设计新材料变得更加困难。材料基因组计划的目标是使用计算机模拟来根据量子物理定律对电子进行建模。这将使研究人员能够设计出具有所需性能的新材料。这个项目的目标是建立快速而准确的计算机程序来模拟这些新材料。这些计划结合了计算机科学、量子化学和凝聚态物理的进步。它们将在开放源码的基于Python的社区代码中实现。这种分布模型允许其他研究人员使用该代码并贡献新的功能。本研究通过开发新的低尺度、高精度方法的并行实现，解决了量子材料模拟中现有软件网络基础设施的空白。特别是，平均场计算的新技术将被开发，这将作为周期耦合团簇和量子蒙特卡罗方法的基础。稀疏性和张量分解方面的最新技术将被用来实现良好的系统规模缩放，同时保持这些数值格式的精度。关键是，这些方法将使用高效的高级软件抽象来开发，这些抽象在利用Cyclops库进行大规模并行执行的PySCF中实现为Python级模块。还将扩展图书馆软件基础设施，以通过源到源的自动区分来最大限度地提高生产率，并使稀疏内核能够在新兴的基于GPU的超级计算架构上执行。该奖项由NSF高级网络基础设施办公室、NSF数学和物理科学局内的材料研究部和化学部共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

How electrons are arranged in materials gives rise to a large variety of different behaviors. We can observe these behaviors and use them in various technologies. However, the prediction of these behaviors is a serious challenge. This makes the successful design of new materials harder. The goal of the Materials Genome Initiative is to use computer simulations to model electrons according to the laws of quantum physics. This will allow researchers to design new materials with desired properties. This project aims to build fast and accurate computer programs which simulate those new materials. These programs combine advances in computer science, quantum chemistry, and condensed-matter physics. They will be implemented in an open-source Python-based community code. This distribution model allows other researchers to use this code and to contribute new features.This research addresses gaps in existing software cyberinfrastructure in quantum materials simulation, by developing novel parallel implementations of low-scaling, high-accuracy methods. In particular, new techniques for mean-field calculations will be developed, which will act as groundwork for periodic coupled-cluster and quantum Monte Carlo methods. State-of-the-art techniques in sparsity and tensor decomposition will be employed to achieve good system-size scaling while retaining accuracy within each of these numerical schemes. Critically, the methods will be developed using efficient high-level software abstractions, implemented as Python-level modules within PySCF that leverage the Cyclops library for massively-parallel execution. The library software infrastructure will also be extended to maximize productivity via source-to-source automatic differentiation, as well as to enable execution of sparse kernels on emerging GPU-based supercomputing architectures. This award is jointly supported by the NSF Office of Advanced Cyberinfrastructure, and the Division of Materials Research and the Division of Chemistry within the NSF Directorate of Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.