arXiv subjects

Aleksandra Leszczyk

Publications and source records attributed to Aleksandra Leszczyk.

4 recordsLinked to original sources

arXiv2023

Accelerating Pythonic coupled cluster implementations: a comparison between CPUs and GPUs

We scrutinize how to accelerate the bottleneck operations of Pythonic coupled cluster implementations performed on a \texttt{NVIDIA} Tesla V100S PCIe 32GB (rev 1a) Graphics Processing Unit (GPU). The \texttt{NVIDIA} Compute Unified Device Architecture (CUDA) API is interacted with via \texttt{CuPy}, an open-source library for Python, designed as a \texttt{NumPy} drop-in replacement for GPUs. The implementation uses the Cholesky linear algebra domain and is done in {PyBEST}, the Pythonic Black-box Electronic Structure Tool -- a fully-fledged modern electronic structure software package. Due to the limitations of Video Memory (VRAM), the GPU calculations must be performed batch-wise. Timing results of some contractions containing large tensors are presented. The \texttt{CuPy} implementation leads to factor 10 speed-up compared to calculations on 36 CPUs. Furthermore, we benchmark several Pythonic routines for time and memory requirements to identify the optimal choice of the tensor contraction operations available. Finally, we compare an example CCSD and pCCD-LCCSD calculation performed solely on CPUs to their CPU--GPU hybrid implementation. Our results indicate a significant speed-up (up to a factor of 16 regarding the bottleneck operations) when offloading specific contractions to the GPU using \texttt{CuPy}.

physics.chem-ph↗

arXiv2021

Assessing the accuracy of tailored coupled cluster methods corrected by electronic wave functions of polynomial cost

Tailored coupled cluster theory represents a computationally inexpensive way to describe static and dynamical electron correlation effects. In this work, we scrutinize the performance of various tailored coupled cluster methods externally corrected by electronic wave functions of polynomial cost. Specifically, we focus on frozen-pair coupled-cluster (fpCC) methods, which are externally corrected by pair-coupled cluster doubles (pCCD), and coupled cluster theory tailored by matrix product state wave functions optimized by the density matrix renormalization group (DMRG) algorithm. As test system, we selected a set of various small- and medium-sized molecules containing diatomics (N$_2$, F$_2$, C$_2$, CN$^+$, BN, BO$^+$, and Cr$_2$) and molecules (ammonia, ethylene, cyclobutadiene, benzene) for which conventional single-reference coupled cluster singles and doubles (CCSD) is not able to produce accurate results for spectroscopic constants, potential energy surfaces, and barrier heights. Most importantly, DMRG-tailored and pCCD-tailored approaches yield similar errors in spectroscopic constants and potential energy surfaces compared to multireference and/or experimental reference data and generally outrank the conventional single-reference CCSD approach. Although fpCC methods provide a reliable description for the dissociation pathway of molecules featuring single and quadruple bonds, they fail in the description of triple or hextuple bond-breaking processes or avoided crossing regions.

physics.chem-ph↗

arXiv2020

Pythonic Black-box Electronic Structure Tool (PyBEST). An open-source Python platform for electronic structure calculations at the interface between chemistry and physics

Pythonic Black-box Electronic Structure Tool (PyBEST) represents a fully-fledged modern electronic structure software package developed at Nicolaus Copernicus University in Toruń. The package provides an efficient and reliable platform for electronic structure calculations at the interface between chemistry and physics using unique electronic structure methods, analysis tools, and visualization. Examples are the (orbital-optimized) pCCD-based models for ground- and excited-states electronic structure calculations as well as the quantum entanglement analysis framework based on the single-orbital entropy and orbital-pair mutual information. PyBEST is written primarily in the Python3 programming language with additional parts written in C++, which are interfaced using Pybind11, a lightweight header-only library. By construction, PyBEST is easy to use, to code, and to interface with other software packages. Moreover, its modularity allows us to conveniently host additional Python packages and software libraries in future releases to enhance its performance. The electronic structure methods available in PyBEST are tested for the half-filled 1-D model Hamiltonian. The capability of PyBEST to perform large-scale electronic structure calculations is demonstrated for the model vitamin B12 compound. The investigated molecule is composed of 190 electrons and 777 orbitals for which an orbital optimization within pCCD and an orbital entanglement and correlation analysis are performed for the first time.

physics.chem-ph↗

arXiv2020

New Strategies in Modeling Electronic Structures and Properties with Applications to Actinides

This chapter discusses contemporary quantum chemical methods and provides general insights into modern electronic structure theory with a focus on heavy-element-containing compounds. We first give a short overview of relativistic Hamiltonians that are frequently applied to account for relativistic effects. Then, we scrutinize various quantum chemistry methods that approximate the $N$-electron wave function. In this respect, we will review the most popular single- and multi-reference approaches that have been developed to model the multi-reference nature of heavy element compounds and their ground- and excited-state electronic structures. Specifically, we introduce various flavors of post-Hartree--Fock methods and optimization schemes like the complete active space self-consistent field method, the configuration interaction approach, the Fock-space coupled cluster model, the pair-coupled cluster doubles ansatz, also known as the antisymmetric product of 1 reference orbital geminal, and the density matrix renormalization group algorithm. Furthermore, we will illustrate how concepts of quantum information theory provide us with a qualitative understanding of complex electronic structures using the picture of interacting orbitals. While modern quantum chemistry facilitates a quantitative description of atoms and molecules as well as their properties, concepts of quantum information theory offer new strategies for a qualitative interpretation that can shed new light onto the chemistry of complex molecular compounds.

physics.chem-ph↗