基于GPU的Hartree-Fock与密度泛函算法及程序

基于图形处理单元(GPU)的算法和程序为解决量子化学中的计算瓶颈开辟了道路. 作者设计了基于GPU的量子化学算法和程序, 实现了Hartree-Fock方法和密度泛函理论中双电子排斥积分计算、Fock矩阵构造以及交换相关泛函的计算. 由于计算内核使用OpenCL编程框架, 程序可以在多种架构的计算设备上执行. 对于不同计算模块和分子自洽场计算的测试表明, 基于OpenCL的GPU程序相比CPU上的串行程序实现了最快148倍的加速.     

基于GPU计算的并行PLS算法研究与实现

偏最小二乘算法(PLS)是与红外、近红外光谱分析结合使用最为广泛的化学计量学算法,然而当前PLS算法通常采用单线程方式实现,当校正模型数量多或样本数量大、波长点数和主成分数较多,模型需对光谱预处理和波长选择方法反复优化时,计算十分缓慢。为大幅提高建模速度,该文提出了一种基于图形处理器(GPU)的并行计算策略,利用具有大规模并行计算特性的GPU作为计算设备,结合CUBLAS库函数实现了基于GPU并行的PLS建模算法(CUPLS)。利用近红外光谱数据集进行性能对比实验,结果表明CUPLS建模算法较传统单线程实现的PLS算法,加速比可达近42倍,极大地提升了化学计量学算法的建模效率。该方法亦可用于其它化学计量学算法的加速。     

分子轨道计算应用于核化学与同位素化学

随着量子化学计算技术的发展,用量子化学来定量地处理和解决科学问题的对象愈加广泛。本文简单地介绍了分子轨道(简称MO,下同)计算应用于核化学、放射化学和同位素化学,诸如对萃取剂性能的理论分析、放射核素衰变对分子稳定性的影响、同位素效应的计算和在核药物化学中的应用。     

基于图形处理单元与密度拟合近似的单精度耦合簇CCSD和CCSD(T)程序

作者此前工作表明, 在耦合簇CCSD (Coupled-Cluster approaches within the singles and doubles approximation)与CCSD(T) (CCSD approaches augmented by a perturbative treatment of triple excitations)计算中结合单精度数与消费型图形处理单元(GPU), 可以显著提高计算速度. 然而由于CCSD(T)计算对内存的巨大需求以及消费型GPU的内存限制, 在利用消费型GPU进行加速时, 不考虑利用空间对称性的情况下, 此前开发的CCSD(T)程序仅能用于计算300~400个基函数的体系. 利用密度拟合(Density-Fitting, DF)处理双电子积分可以显著降低CCSD(T)计算过程中的内存需求, 本工作发展了基于密度拟合近似并结合单精度数进行运算的DF-CCSD(T)程序, 该程序可用于包含700个基函数的无对称性体系的单点能计算, 以及包含1700个基函数的有对称性体系. 本工作所使用的计算节点配置了型号为Intel I9-10900k的CPU和型号为RTX3090的GPU, 与用双精度数在CPU上的计算相比, 利用单精度数结合GPU进行运算可以将CCSD的计算速度提升16倍, (T)部分可提升40倍左右, 而使用单精度数引入的误差可忽略不计. 在程序开发过程中, 作者发展了一套可利用GPU或CPU结合单精度数或双精度数进行含空间对称性的矩阵操作代码库. 基于该套代码库, 可以显著降低开发含空间对称性的耦合簇代码的难度.     

量子化学的第二次革命——1998年诺贝尔化学奖简介

简单介绍了１９９８年诺贝尔化学奖获得者以及３０多年来他们在发展量子化学理论与计算方法方面的卓越贡献,文中还简要地介绍了量子化学计算在化学中的应用。     

量子化学计算在大学有机化学教学中的应用和实践

量子化学计算通过对具体分子进行系统的理论分析和计算,能比较准确和形象地反映分子的稳定性、化学反应机理等基本化学问题。以1-丙基-4-氯环己烷结构稳定性判断和硼氢化钠还原4-丙基环己酮机理解析为例,探讨了量子化学计算在大学有机化学教学中的应用。量子化学计算在大学有机化学教学中的使用有助于学生了解分子结构和理解化学反应机理,丰富课堂教学的形式,增强课堂教学的生动性,激发学生的学习兴趣。     

量子化学的第二次革命——1998年诺贝尔化学奖简介

简单介绍了１９９８年诺贝尔化学奖获得者以及３０多年来他们在发展量子化学理论与计算方法方面的卓越贡献。文中还简要地介绍了量子化学计算在化学中的应用。     

量子化学计算辅助化学教学的探索与实践

量子化学的发展不仅为化学教学提供了形象而生动的教学案例,而且也为化学概念和现象提供了具体可视化的解释。以“科研教学互动”为核心,通过具体的量子化学前沿成果辅助教学应用实例、量子化学计算软件辅助教学等方面阐述了量子化学计算在大学化学教学中的应用前景。这种将量子化学计算与化学教学相结合的教育方式,将对化学课程的发展、化学教学的改革及学生科研创新能力的培养起到推动作用。     

含时密度矩阵重正化群的理论与应用

密度矩阵重正化群(DMRG)作为计算低维强关联体系强有力的方法为人熟知, 在量子化学电子结构计算中得到广泛应用. 最近几年, 含时密度矩阵重正化群(TD-DMRG)的理论取得较快发展, TD-DMRG逐渐成为复杂体系量子动力学理论模拟的重要新兴方法之一. 本文综述了基于矩阵乘积态(MPS) 和矩阵乘积算符(MPO)的DMRG基本理论, 并重点介绍了若干最常见的TD-DMRG时间演化算法, 包括基于演化再压缩(P&C) 的算法、 基于含时变分原理(TDVP)的算法和时间步瞄准(TST)算法; 还对利用TD-DMRG模拟有限温体系的纯化(Purification)算法和最小纠缠典型量子热态(METTS)算法进行了介绍. 最后, 对近年TD-DMRG在复杂体系量子动力学中的应用进行了总结.     

N,N-二(2-苯并咪唑亚甲基)甲胺高氯酸盐的晶体和电子结构

合成了三齿配体N,N-二(2-苯并咪唑亚甲基)甲胺(MN3)的高氯酸盐.对该化合物进行了元素分析、紫外和红外表征;采用X射线衍射方法测定了该化合物的晶体结构,依据晶体结构数据使用G98程序对配体(MN3)进行了量子化学计算.     

GPUs as boosters to analyze scalar and vector fields in quantum chemistry

The analysis of scalar and vector fields in quantum chemistry is an essential task for the computational chemistry community, where such quantities must be evaluated rapidly to perform a particular study. For example, the atoms in molecules approach proposed by Bader has become popular; however, this method demands significant computational resources to compute the involved tasks in short times. In this article, we discuss the importance of graphics processing units (GPU) to analyze electron density, and related fields, implementing several scalar, and vector fields within the graphics processing units for atoms and molecules (GPUAM) code developed by a group of the Universidad Autónoma Metropolitana in México City. With this application, the quantum chemistry community can perform demanding computational tasks on a desktop, where CPUs and GPUs are used to their maximum capabilities. The performance of GPUAM is tested in several systems and over different GPUs, where a GPU installed in a workstation converts it to a robust high-performance computing system.     

Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models

The conductor-like polarizable continuum model (C-PCM) with switching/Gaussian smooth discretization is a widely used implicit solvation model in quantum chemistry. We have previously implemented C-PCM solvation for Hartree-Fock (HF) and density functional theory (DFT) on graphical processing units (GPUs), enabling the quantum mechanical treatment of large solvated biomolecules. Here, we first propose a GPU-based algorithm for the PCM conjugate gradient linear solver that greatly improves the performance for very large molecules. The overhead for PCM-related evaluations now consumes less than 15% of the total runtime for DFT calculations on large molecules. Second, we demonstrate that our algorithms tailored for ground state C-PCM are transferable to excited state properties. Using a single GPU, our method evaluates the analytic gradient of the linear response PCM time-dependent density functional theory energy up to 80× faster than a conventional central processing unit (CPU)-based implementation. In addition, our C-PCM algorithms are transferable to other methods that require electrostatic potential (ESP) evaluations. For example, we achieve speed-ups of up to 130× for restricted ESP-based atomic charge evaluations, when compared to CPU-based codes. We also summarize and compare the different PCM cavity discretization schemes used in some popular quantum chemistry packages as a reference for both users and developers.     

Linear‐scaling self‐consistent field calculations based on divide‐and‐conquer method using resolution‐of‐identity approximation on graphical processing units

Graphical processing units (GPUs) are emerging in computational chemistry to include Hartree?Fock (HF) methods and electron‐correlation theories. However, ab initio calculations of large molecules face technical difficulties such as slow memory access between central processing unit and GPU and other shortfalls of GPU memory. The divide‐and‐conquer (DC) method, which is a linear‐scaling scheme that divides a total system into several fragments, could avoid these bottlenecks by separately solving local equations in individual fragments. In addition, the resolution‐of‐the‐identity (RI) approximation enables an effective reduction in computational cost with respect to the GPU memory. The present study implemented the DC‐RI‐HF code on GPUs using math libraries, which guarantee compatibility with future development of the GPU architecture. Numerical applications confirmed that the present code using GPUs significantly accelerated the HF calculations while maintaining accuracy. © 2014 Wiley Periodicals, Inc.     

New algorithm for tensor contractions on multi‐core CPUs,GPUs, and accelerators enables CCSD and EOM‐CCSD calculations with over 1000 basis functions on a single compute node

A new hardware‐agnostic contraction algorithm for tensors of arbitrary symmetry and sparsity is presented. The algorithm is implemented as a stand‐alone open‐source code libxm . This code is also integrated with general tensor library libtensor and with the Q‐Chem quantum‐chemistry package. An overview of the algorithm, its implementation, and benchmarks are presented. Similarly to other tensor software, the algorithm exploits efficient matrix multiplication libraries and assumes that tensors are stored in a block‐tensor form. The distinguishing features of the algorithm are: (i) efficient repackaging of the individual blocks into large matrices and back, which affords efficient graphics processing unit (GPU)‐enabled calculations without modifications of higher‐level codes; (ii) fully asynchronous data transfer between disk storage and fast memory. The algorithm enables canonical all‐electron coupled‐cluster and equation‐of‐motion coupled‐cluster calculations with single and double substitutions (CCSD and EOM‐CCSD) with over 1000 basis functions on a single quad‐GPU machine. We show that the algorithm exhibits predicted theoretical scaling for canonical CCSD calculations, O (N ⁶), irrespective of the data size on disk. © 2017 Wiley Periodicals, Inc.     

量子控制论在化学中的应用

控制量子现象是化学研究中的一个重要目标,量子控制论对实现该目标具有积极的指导意义.本文综述了量子控制论在化学中的应用及其进展,重点分析了量子相干控制、量子优化控制、闭环学习控制和能控性观念在化学研究中的应用,介绍了它们的研究现状,并对其未来研究进行了展望.     

Atoms and bonds in molecules and chemical explanations

The concepts of atoms and bonds in molecules which appeared in chemistry during the nineteenth century are unavoidable to explain the structure and the reactivity of the matter at a chemical level of understanding. Although they can be criticized from a strict reductionist point of view, because neither atoms nor bonds are observable in the sense of quantum mechanics, the topological and statistical interpretative approaches of quantum chemistry (quantum theory of atoms in molecules, electron localization function and maximum probability domain) provide consistent definitions which accommodate chemistry and quantum mechanics.     

量子化学方法在锂离子电池电解液研究中的应用

总结了近年来量子化学方法在锂离子电池电解液研究中的应用进展,阐述了量子化学方法在新型锂盐设计、功能添加剂作用机理分析和电极/电解液界面膜的形成过程研究中发挥的作用,对其用来设计锂离子电池电解液功能分子作出展望。     

Use of Quantum Chemical Methods to Study Cyclodextrin Chemistry

Studies of cyclodextrin chemistry by quantum chemical methods are briefly surveyed. Emphases are put on what types of quantum chemical methods can be used for cyclodextrin chemistry, how to use quantum chemical methods to find the global minimum, to study the structures, binding energies, driving forces for cyclodextrin complexes, as well as chemical reactions occurring inside cyclodextrin cavities. Problems associated with the application of quantum chemical methods in cyclodextrin chemistry are also discussed.     

生物大分子体系量子化学计算方法新进展

本文就近年来报道的4 种研究生物大分子体系的量子化学计算方法(计算显微镜方法、定域分子轨道方法、线性标度半经验量子化学方法和并行算法) 作了较为详细的介绍, 并展望了该领域的研究前景。